{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 主要包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import random\n",
    "\n",
    "# 绘图\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 集成学习中的stacking\n",
    "from mlxtend.regressor import StackingCVRegressor\n",
    "import lightgbm as lgb\n",
    "from lightgbm import LGBMRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# Stats\n",
    "# skew度量倾斜度(偏度)(三阶中心距),和正态化\n",
    "from scipy.stats import skew, norm\n",
    "# scipy中的方法\n",
    "from scipy.special import boxcox1p\n",
    "from scipy.stats import boxcox_normmax\n",
    "\n",
    "# 混杂的\n",
    "# 网格搜索最优模型\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# 交叉验证,交叉验证的评估分数\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "# mse\n",
    "from sklearn.metrics import mean_squared_error\n",
    "# 将分类整数特征编码为即“one-of-K”或“dummy”\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "# 它还可以用于将非数字标签(只要它们是可耐洗和可比较的)转换为数字标签。\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# 生成管道\n",
    "from sklearn.pipeline import make_pipeline\n",
    "# StandardScaler和scale的区别是使用StandardScaler会保留测试集合均值和标准差同样在测试集上进行标准化\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 使用对异常值具有鲁棒性的统计数据来缩放特性。\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "# 降维\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['font.sans-serif']=['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# 忽略浸膏\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\")\n",
    "pd.options.display.max_seq_items = 8000\n",
    "pd.options.display.max_rows = 8000\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200000, 202), (200000, 201))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载数据集\n",
    "train = pd.read_csv('input/train.csv')\n",
    "test = pd.read_csv('input/test.csv')\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200000, 202), (200000, 201))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train1 = train.iloc[:2000,:]\n",
    "# test1 = test.iloc[:2000,:]\n",
    "train1 = train.copy()\n",
    "test1 = test.copy()\n",
    "train1.shape, test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
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       "      <th>var_2</th>\n",
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       "      <th>var_83</th>\n",
       "      <th>var_84</th>\n",
       "      <th>var_85</th>\n",
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       "      <th>var_88</th>\n",
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       "      <th>var_185</th>\n",
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       "      <th>var_187</th>\n",
       "      <th>var_188</th>\n",
       "      <th>var_189</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.100490</td>\n",
       "      <td>10.679914</td>\n",
       "      <td>-1.627622</td>\n",
       "      <td>10.715192</td>\n",
       "      <td>6.796529</td>\n",
       "      <td>11.078333</td>\n",
       "      <td>-5.065317</td>\n",
       "      <td>5.408949</td>\n",
       "      <td>16.545850</td>\n",
       "      <td>0.284162</td>\n",
       "      <td>7.567236</td>\n",
       "      <td>0.394340</td>\n",
       "      <td>-3.245596</td>\n",
       "      <td>14.023978</td>\n",
       "      <td>8.530232</td>\n",
       "      <td>7.537606</td>\n",
       "      <td>14.573126</td>\n",
       "      <td>9.333264</td>\n",
       "      <td>-5.696731</td>\n",
       "      <td>15.244013</td>\n",
       "      <td>12.438567</td>\n",
       "      <td>13.290894</td>\n",
       "      <td>17.257883</td>\n",
       "      <td>4.305430</td>\n",
       "      <td>3.019540</td>\n",
       "      <td>10.584400</td>\n",
       "      <td>13.667496</td>\n",
       "      <td>-4.055133</td>\n",
       "      <td>-1.137908</td>\n",
       "      <td>5.532980</td>\n",
       "      <td>5.053874</td>\n",
       "      <td>-7.687740</td>\n",
       "      <td>10.393046</td>\n",
       "      <td>-0.512886</td>\n",
       "      <td>14.774147</td>\n",
       "      <td>11.434250</td>\n",
       "      <td>3.842499</td>\n",
       "      <td>2.187230</td>\n",
       "      <td>5.868899</td>\n",
       "      <td>10.642131</td>\n",
       "      <td>0.662956</td>\n",
       "      <td>-6.725505</td>\n",
       "      <td>9.299858</td>\n",
       "      <td>11.222356</td>\n",
       "      <td>11.569954</td>\n",
       "      <td>8.948289</td>\n",
       "      <td>-12.699667</td>\n",
       "      <td>11.326488</td>\n",
       "      <td>-12.471737</td>\n",
       "      <td>14.704713</td>\n",
       "      <td>16.682499</td>\n",
       "      <td>12.740986</td>\n",
       "      <td>13.428912</td>\n",
       "      <td>-2.528816</td>\n",
       "      <td>6.008569</td>\n",
       "      <td>1.137117</td>\n",
       "      <td>12.745852</td>\n",
       "      <td>16.629165</td>\n",
       "      <td>6.272014</td>\n",
       "      <td>3.177633</td>\n",
       "      <td>8.931124</td>\n",
       "      <td>12.155618</td>\n",
       "      <td>-11.946744</td>\n",
       "      <td>0.874170</td>\n",
       "      <td>0.661173</td>\n",
       "      <td>6.369157</td>\n",
       "      <td>0.982891</td>\n",
       "      <td>5.794039</td>\n",
       "      <td>11.943223</td>\n",
       "      <td>5.018893</td>\n",
       "      <td>-3.331515</td>\n",
       "      <td>24.446811</td>\n",
       "      <td>0.669756</td>\n",
       "      <td>0.640553</td>\n",
       "      <td>19.610888</td>\n",
       "      <td>19.518846</td>\n",
       "      <td>16.853732</td>\n",
       "      <td>6.050871</td>\n",
       "      <td>19.066993</td>\n",
       "      <td>5.349479</td>\n",
       "      <td>14.402136</td>\n",
       "      <td>5.795044</td>\n",
       "      <td>14.719024</td>\n",
       "      <td>-3.471273</td>\n",
       "      <td>1.025817</td>\n",
       "      <td>-2.590209</td>\n",
       "      <td>18.362721</td>\n",
       "      <td>5.621058</td>\n",
       "      <td>11.351483</td>\n",
       "      <td>8.702924</td>\n",
       "      <td>3.725208</td>\n",
       "      <td>-16.548147</td>\n",
       "      <td>6.987541</td>\n",
       "      <td>12.739578</td>\n",
       "      <td>10.556740</td>\n",
       "      <td>10.999162</td>\n",
       "      <td>-0.084344</td>\n",
       "      <td>14.400433</td>\n",
       "      <td>18.539645</td>\n",
       "      <td>1.752012</td>\n",
       "      <td>-0.746296</td>\n",
       "      <td>-6.600518</td>\n",
       "      <td>13.413526</td>\n",
       "      <td>22.294908</td>\n",
       "      <td>1.568393</td>\n",
       "      <td>11.509834</td>\n",
       "      <td>4.244744</td>\n",
       "      <td>8.617657</td>\n",
       "      <td>17.796266</td>\n",
       "      <td>14.224435</td>\n",
       "      <td>18.458001</td>\n",
       "      <td>5.513238</td>\n",
       "      <td>6.312603</td>\n",
       "      <td>3.317843</td>\n",
       "      <td>8.136542</td>\n",
       "      <td>3.081191</td>\n",
       "      <td>2.213717</td>\n",
       "      <td>2.402570</td>\n",
       "      <td>16.102233</td>\n",
       "      <td>-5.305132</td>\n",
       "      <td>3.032849</td>\n",
       "      <td>24.521078</td>\n",
       "      <td>11.310591</td>\n",
       "      <td>1.192984</td>\n",
       "      <td>7.076254</td>\n",
       "      <td>4.272740</td>\n",
       "      <td>12.489165</td>\n",
       "      <td>13.202326</td>\n",
       "      <td>0.851507</td>\n",
       "      <td>-1.127952</td>\n",
       "      <td>15.460314</td>\n",
       "      <td>12.257151</td>\n",
       "      <td>0.544674</td>\n",
       "      <td>7.799676</td>\n",
       "      <td>6.813270</td>\n",
       "      <td>-4.826053</td>\n",
       "      <td>-4.259472</td>\n",
       "      <td>22.968602</td>\n",
       "      <td>17.613651</td>\n",
       "      <td>1.210792</td>\n",
       "      <td>7.760193</td>\n",
       "      <td>3.423636</td>\n",
       "      <td>2.897596</td>\n",
       "      <td>11.983489</td>\n",
       "      <td>12.333698</td>\n",
       "      <td>8.647632</td>\n",
       "      <td>4.841328</td>\n",
       "      <td>10.341178</td>\n",
       "      <td>-3.300779</td>\n",
       "      <td>3.990726</td>\n",
       "      <td>5.296237</td>\n",
       "      <td>16.817671</td>\n",
       "      <td>10.141542</td>\n",
       "      <td>7.633199</td>\n",
       "      <td>16.727902</td>\n",
       "      <td>6.974955</td>\n",
       "      <td>-2.074128</td>\n",
       "      <td>13.209272</td>\n",
       "      <td>-4.813552</td>\n",
       "      <td>17.914591</td>\n",
       "      <td>10.223282</td>\n",
       "      <td>24.259300</td>\n",
       "      <td>5.633293</td>\n",
       "      <td>5.362896</td>\n",
       "      <td>11.002170</td>\n",
       "      <td>-2.871906</td>\n",
       "      <td>19.315753</td>\n",
       "      <td>2.963335</td>\n",
       "      <td>-4.151155</td>\n",
       "      <td>4.937124</td>\n",
       "      <td>5.636008</td>\n",
       "      <td>-0.004962</td>\n",
       "      <td>-0.831777</td>\n",
       "      <td>19.817094</td>\n",
       "      <td>-0.677967</td>\n",
       "      <td>20.210677</td>\n",
       "      <td>11.640613</td>\n",
       "      <td>-2.799585</td>\n",
       "      <td>11.882933</td>\n",
       "      <td>-1.014064</td>\n",
       "      <td>2.591444</td>\n",
       "      <td>-2.741666</td>\n",
       "      <td>10.085518</td>\n",
       "      <td>0.719109</td>\n",
       "      <td>8.769088</td>\n",
       "      <td>12.756676</td>\n",
       "      <td>-3.983261</td>\n",
       "      <td>8.970274</td>\n",
       "      <td>-10.335043</td>\n",
       "      <td>15.377174</td>\n",
       "      <td>0.746072</td>\n",
       "      <td>3.234440</td>\n",
       "      <td>7.438408</td>\n",
       "      <td>1.927839</td>\n",
       "      <td>3.331774</td>\n",
       "      <td>17.993784</td>\n",
       "      <td>-0.142088</td>\n",
       "      <td>2.303335</td>\n",
       "      <td>8.908158</td>\n",
       "      <td>15.870720</td>\n",
       "      <td>-3.326537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.300653</td>\n",
       "      <td>3.040051</td>\n",
       "      <td>4.050044</td>\n",
       "      <td>2.640894</td>\n",
       "      <td>2.043319</td>\n",
       "      <td>1.623150</td>\n",
       "      <td>7.863267</td>\n",
       "      <td>0.866607</td>\n",
       "      <td>3.418076</td>\n",
       "      <td>3.332634</td>\n",
       "      <td>1.235070</td>\n",
       "      <td>5.500793</td>\n",
       "      <td>5.970253</td>\n",
       "      <td>0.190059</td>\n",
       "      <td>4.639536</td>\n",
       "      <td>2.247908</td>\n",
       "      <td>0.411711</td>\n",
       "      <td>2.557421</td>\n",
       "      <td>6.712612</td>\n",
       "      <td>7.851370</td>\n",
       "      <td>7.996694</td>\n",
       "      <td>5.876254</td>\n",
       "      <td>8.196564</td>\n",
       "      <td>2.847958</td>\n",
       "      <td>0.526893</td>\n",
       "      <td>3.777245</td>\n",
       "      <td>0.285535</td>\n",
       "      <td>5.922210</td>\n",
       "      <td>1.523714</td>\n",
       "      <td>0.783367</td>\n",
       "      <td>2.615942</td>\n",
       "      <td>7.965198</td>\n",
       "      <td>2.159891</td>\n",
       "      <td>2.587830</td>\n",
       "      <td>4.322325</td>\n",
       "      <td>0.541614</td>\n",
       "      <td>5.179559</td>\n",
       "      <td>3.119978</td>\n",
       "      <td>2.249730</td>\n",
       "      <td>4.278903</td>\n",
       "      <td>4.068845</td>\n",
       "      <td>8.279259</td>\n",
       "      <td>5.938088</td>\n",
       "      <td>0.695991</td>\n",
       "      <td>0.309599</td>\n",
       "      <td>5.903073</td>\n",
       "      <td>21.404912</td>\n",
       "      <td>2.860511</td>\n",
       "      <td>10.579862</td>\n",
       "      <td>11.384332</td>\n",
       "      <td>7.855762</td>\n",
       "      <td>0.691709</td>\n",
       "      <td>8.187306</td>\n",
       "      <td>4.985532</td>\n",
       "      <td>0.764753</td>\n",
       "      <td>8.414241</td>\n",
       "      <td>5.690072</td>\n",
       "      <td>3.540174</td>\n",
       "      <td>0.795026</td>\n",
       "      <td>4.296686</td>\n",
       "      <td>0.854798</td>\n",
       "      <td>4.222389</td>\n",
       "      <td>11.622948</td>\n",
       "      <td>2.026238</td>\n",
       "      <td>3.113089</td>\n",
       "      <td>1.485854</td>\n",
       "      <td>3.786493</td>\n",
       "      <td>1.121366</td>\n",
       "      <td>7.365115</td>\n",
       "      <td>0.007186</td>\n",
       "      <td>3.955723</td>\n",
       "      <td>11.951742</td>\n",
       "      <td>0.266696</td>\n",
       "      <td>3.944703</td>\n",
       "      <td>7.466303</td>\n",
       "      <td>14.112591</td>\n",
       "      <td>6.055322</td>\n",
       "      <td>7.938351</td>\n",
       "      <td>3.817292</td>\n",
       "      <td>1.993792</td>\n",
       "      <td>1.309055</td>\n",
       "      <td>7.436737</td>\n",
       "      <td>2.299567</td>\n",
       "      <td>8.479255</td>\n",
       "      <td>8.297229</td>\n",
       "      <td>6.225305</td>\n",
       "      <td>3.908536</td>\n",
       "      <td>7.751142</td>\n",
       "      <td>5.661867</td>\n",
       "      <td>2.491460</td>\n",
       "      <td>3.560554</td>\n",
       "      <td>13.152810</td>\n",
       "      <td>0.152641</td>\n",
       "      <td>4.186252</td>\n",
       "      <td>0.543341</td>\n",
       "      <td>2.768099</td>\n",
       "      <td>0.621125</td>\n",
       "      <td>8.525400</td>\n",
       "      <td>12.642382</td>\n",
       "      <td>0.715836</td>\n",
       "      <td>1.862550</td>\n",
       "      <td>9.181683</td>\n",
       "      <td>4.950537</td>\n",
       "      <td>8.628179</td>\n",
       "      <td>0.185020</td>\n",
       "      <td>1.970520</td>\n",
       "      <td>0.855698</td>\n",
       "      <td>1.894899</td>\n",
       "      <td>7.604723</td>\n",
       "      <td>0.171091</td>\n",
       "      <td>4.355031</td>\n",
       "      <td>3.823253</td>\n",
       "      <td>1.082404</td>\n",
       "      <td>1.591170</td>\n",
       "      <td>4.459077</td>\n",
       "      <td>0.985396</td>\n",
       "      <td>2.621851</td>\n",
       "      <td>1.650912</td>\n",
       "      <td>13.297662</td>\n",
       "      <td>8.799268</td>\n",
       "      <td>4.182796</td>\n",
       "      <td>12.121016</td>\n",
       "      <td>1.714416</td>\n",
       "      <td>5.168479</td>\n",
       "      <td>6.147345</td>\n",
       "      <td>2.736821</td>\n",
       "      <td>0.318100</td>\n",
       "      <td>0.776056</td>\n",
       "      <td>3.137684</td>\n",
       "      <td>3.238043</td>\n",
       "      <td>4.136453</td>\n",
       "      <td>0.832199</td>\n",
       "      <td>0.456280</td>\n",
       "      <td>1.456486</td>\n",
       "      <td>0.375603</td>\n",
       "      <td>6.166126</td>\n",
       "      <td>7.617732</td>\n",
       "      <td>10.382235</td>\n",
       "      <td>8.890516</td>\n",
       "      <td>4.551750</td>\n",
       "      <td>7.686433</td>\n",
       "      <td>4.896325</td>\n",
       "      <td>6.715637</td>\n",
       "      <td>5.691936</td>\n",
       "      <td>2.934706</td>\n",
       "      <td>0.922469</td>\n",
       "      <td>3.899281</td>\n",
       "      <td>2.518883</td>\n",
       "      <td>7.413301</td>\n",
       "      <td>0.199192</td>\n",
       "      <td>10.385133</td>\n",
       "      <td>2.464157</td>\n",
       "      <td>3.962426</td>\n",
       "      <td>3.005373</td>\n",
       "      <td>2.014200</td>\n",
       "      <td>4.961678</td>\n",
       "      <td>5.771261</td>\n",
       "      <td>0.955140</td>\n",
       "      <td>5.570272</td>\n",
       "      <td>7.885579</td>\n",
       "      <td>4.122912</td>\n",
       "      <td>10.880263</td>\n",
       "      <td>0.217938</td>\n",
       "      <td>1.419612</td>\n",
       "      <td>5.262056</td>\n",
       "      <td>5.457784</td>\n",
       "      <td>5.024182</td>\n",
       "      <td>0.369684</td>\n",
       "      <td>7.798020</td>\n",
       "      <td>3.105986</td>\n",
       "      <td>0.369437</td>\n",
       "      <td>4.424621</td>\n",
       "      <td>5.378008</td>\n",
       "      <td>8.674171</td>\n",
       "      <td>5.966674</td>\n",
       "      <td>7.136427</td>\n",
       "      <td>2.892167</td>\n",
       "      <td>7.513939</td>\n",
       "      <td>2.628895</td>\n",
       "      <td>8.579810</td>\n",
       "      <td>2.798956</td>\n",
       "      <td>5.261243</td>\n",
       "      <td>1.371862</td>\n",
       "      <td>8.963434</td>\n",
       "      <td>4.474924</td>\n",
       "      <td>9.318280</td>\n",
       "      <td>4.725167</td>\n",
       "      <td>3.189759</td>\n",
       "      <td>11.574708</td>\n",
       "      <td>3.944604</td>\n",
       "      <td>0.976348</td>\n",
       "      <td>4.559922</td>\n",
       "      <td>3.023272</td>\n",
       "      <td>1.478423</td>\n",
       "      <td>3.992030</td>\n",
       "      <td>3.135162</td>\n",
       "      <td>1.429372</td>\n",
       "      <td>5.454369</td>\n",
       "      <td>0.921625</td>\n",
       "      <td>3.010945</td>\n",
       "      <td>10.438015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408400</td>\n",
       "      <td>-15.043400</td>\n",
       "      <td>2.117100</td>\n",
       "      <td>-0.040200</td>\n",
       "      <td>5.074800</td>\n",
       "      <td>-32.562600</td>\n",
       "      <td>2.347300</td>\n",
       "      <td>5.349700</td>\n",
       "      <td>-10.505500</td>\n",
       "      <td>3.970500</td>\n",
       "      <td>-20.731300</td>\n",
       "      <td>-26.095000</td>\n",
       "      <td>13.434600</td>\n",
       "      <td>-6.011100</td>\n",
       "      <td>1.013300</td>\n",
       "      <td>13.076900</td>\n",
       "      <td>0.635100</td>\n",
       "      <td>-33.380200</td>\n",
       "      <td>-10.664200</td>\n",
       "      <td>-12.402500</td>\n",
       "      <td>-5.432200</td>\n",
       "      <td>-10.089000</td>\n",
       "      <td>-5.322500</td>\n",
       "      <td>1.209800</td>\n",
       "      <td>-0.678400</td>\n",
       "      <td>12.720000</td>\n",
       "      <td>-24.243100</td>\n",
       "      <td>-6.166800</td>\n",
       "      <td>2.089600</td>\n",
       "      <td>-4.787200</td>\n",
       "      <td>-34.798400</td>\n",
       "      <td>2.140600</td>\n",
       "      <td>-8.986100</td>\n",
       "      <td>1.508500</td>\n",
       "      <td>9.816900</td>\n",
       "      <td>-16.513600</td>\n",
       "      <td>-8.095100</td>\n",
       "      <td>-1.183400</td>\n",
       "      <td>-6.337100</td>\n",
       "      <td>-14.545700</td>\n",
       "      <td>-35.211700</td>\n",
       "      <td>-8.535900</td>\n",
       "      <td>8.859000</td>\n",
       "      <td>10.652800</td>\n",
       "      <td>-9.939600</td>\n",
       "      <td>-90.252500</td>\n",
       "      <td>1.206200</td>\n",
       "      <td>-47.686200</td>\n",
       "      <td>-23.902200</td>\n",
       "      <td>-8.070700</td>\n",
       "      <td>10.385500</td>\n",
       "      <td>-15.046200</td>\n",
       "      <td>-24.721400</td>\n",
       "      <td>3.344900</td>\n",
       "      <td>-26.778600</td>\n",
       "      <td>-3.782600</td>\n",
       "      <td>2.761800</td>\n",
       "      <td>3.442300</td>\n",
       "      <td>-12.600900</td>\n",
       "      <td>6.184000</td>\n",
       "      <td>-2.100600</td>\n",
       "      <td>-48.802700</td>\n",
       "      <td>-6.328900</td>\n",
       "      <td>-10.554400</td>\n",
       "      <td>1.611700</td>\n",
       "      <td>-14.088800</td>\n",
       "      <td>1.336800</td>\n",
       "      <td>-19.544300</td>\n",
       "      <td>4.993800</td>\n",
       "      <td>-16.309400</td>\n",
       "      <td>-17.027500</td>\n",
       "      <td>-0.224000</td>\n",
       "      <td>-12.383400</td>\n",
       "      <td>-1.665800</td>\n",
       "      <td>-34.101500</td>\n",
       "      <td>-1.293600</td>\n",
       "      <td>-21.633300</td>\n",
       "      <td>7.425700</td>\n",
       "      <td>-1.818300</td>\n",
       "      <td>10.445400</td>\n",
       "      <td>-18.042200</td>\n",
       "      <td>7.586500</td>\n",
       "      <td>-30.026600</td>\n",
       "      <td>-24.220100</td>\n",
       "      <td>-24.439800</td>\n",
       "      <td>7.023000</td>\n",
       "      <td>-19.272200</td>\n",
       "      <td>-8.481600</td>\n",
       "      <td>1.350200</td>\n",
       "      <td>-9.601400</td>\n",
       "      <td>-61.718000</td>\n",
       "      <td>6.521800</td>\n",
       "      <td>-1.018500</td>\n",
       "      <td>8.491600</td>\n",
       "      <td>2.819000</td>\n",
       "      <td>-2.432400</td>\n",
       "      <td>-12.158400</td>\n",
       "      <td>-21.740000</td>\n",
       "      <td>-0.603500</td>\n",
       "      <td>-7.280600</td>\n",
       "      <td>-39.179100</td>\n",
       "      <td>0.075700</td>\n",
       "      <td>-7.382900</td>\n",
       "      <td>0.979300</td>\n",
       "      <td>4.084600</td>\n",
       "      <td>0.715300</td>\n",
       "      <td>0.942400</td>\n",
       "      <td>-5.898000</td>\n",
       "      <td>13.729000</td>\n",
       "      <td>5.769700</td>\n",
       "      <td>-9.239800</td>\n",
       "      <td>2.194200</td>\n",
       "      <td>-2.030200</td>\n",
       "      <td>-5.513900</td>\n",
       "      <td>-0.050500</td>\n",
       "      <td>-6.858600</td>\n",
       "      <td>-3.163000</td>\n",
       "      <td>-31.836900</td>\n",
       "      <td>-37.527700</td>\n",
       "      <td>-9.774200</td>\n",
       "      <td>-18.696200</td>\n",
       "      <td>6.305200</td>\n",
       "      <td>-15.194000</td>\n",
       "      <td>-12.405900</td>\n",
       "      <td>-7.053800</td>\n",
       "      <td>11.486100</td>\n",
       "      <td>11.265400</td>\n",
       "      <td>-8.876900</td>\n",
       "      <td>-11.755900</td>\n",
       "      <td>2.186300</td>\n",
       "      <td>9.528300</td>\n",
       "      <td>-0.954800</td>\n",
       "      <td>2.890000</td>\n",
       "      <td>5.359300</td>\n",
       "      <td>-24.254600</td>\n",
       "      <td>-31.380800</td>\n",
       "      <td>-9.949300</td>\n",
       "      <td>-9.851000</td>\n",
       "      <td>-16.468400</td>\n",
       "      <td>-21.274300</td>\n",
       "      <td>-15.459500</td>\n",
       "      <td>-16.693700</td>\n",
       "      <td>-7.108000</td>\n",
       "      <td>2.806800</td>\n",
       "      <td>5.444300</td>\n",
       "      <td>-8.273400</td>\n",
       "      <td>0.427400</td>\n",
       "      <td>-29.984000</td>\n",
       "      <td>3.320500</td>\n",
       "      <td>-41.168300</td>\n",
       "      <td>9.242000</td>\n",
       "      <td>-2.191500</td>\n",
       "      <td>-2.880000</td>\n",
       "      <td>11.030800</td>\n",
       "      <td>-8.196600</td>\n",
       "      <td>-21.840900</td>\n",
       "      <td>9.996500</td>\n",
       "      <td>-22.990400</td>\n",
       "      <td>-4.554400</td>\n",
       "      <td>-4.641600</td>\n",
       "      <td>-7.452200</td>\n",
       "      <td>4.852600</td>\n",
       "      <td>0.623100</td>\n",
       "      <td>-6.531700</td>\n",
       "      <td>-19.997700</td>\n",
       "      <td>3.816700</td>\n",
       "      <td>1.851200</td>\n",
       "      <td>-35.969500</td>\n",
       "      <td>-5.250200</td>\n",
       "      <td>4.258800</td>\n",
       "      <td>-14.506000</td>\n",
       "      <td>-22.479300</td>\n",
       "      <td>-11.453300</td>\n",
       "      <td>-22.748700</td>\n",
       "      <td>-2.995300</td>\n",
       "      <td>3.241500</td>\n",
       "      <td>-29.116500</td>\n",
       "      <td>4.952100</td>\n",
       "      <td>-29.273400</td>\n",
       "      <td>-7.856100</td>\n",
       "      <td>-22.037400</td>\n",
       "      <td>5.416500</td>\n",
       "      <td>-26.001100</td>\n",
       "      <td>-4.808200</td>\n",
       "      <td>-18.489700</td>\n",
       "      <td>-22.583300</td>\n",
       "      <td>-3.022300</td>\n",
       "      <td>-47.753600</td>\n",
       "      <td>4.412300</td>\n",
       "      <td>-2.554300</td>\n",
       "      <td>-14.093300</td>\n",
       "      <td>-2.691700</td>\n",
       "      <td>-3.814500</td>\n",
       "      <td>-11.783400</td>\n",
       "      <td>8.694400</td>\n",
       "      <td>-5.261000</td>\n",
       "      <td>-14.209600</td>\n",
       "      <td>5.960600</td>\n",
       "      <td>6.299300</td>\n",
       "      <td>-38.852800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.453850</td>\n",
       "      <td>-4.740025</td>\n",
       "      <td>8.722475</td>\n",
       "      <td>5.254075</td>\n",
       "      <td>9.883175</td>\n",
       "      <td>-11.200350</td>\n",
       "      <td>4.767700</td>\n",
       "      <td>13.943800</td>\n",
       "      <td>-2.317800</td>\n",
       "      <td>6.618800</td>\n",
       "      <td>-3.594950</td>\n",
       "      <td>-7.510600</td>\n",
       "      <td>13.894000</td>\n",
       "      <td>5.072800</td>\n",
       "      <td>5.781875</td>\n",
       "      <td>14.262800</td>\n",
       "      <td>7.452275</td>\n",
       "      <td>-10.476225</td>\n",
       "      <td>9.177950</td>\n",
       "      <td>6.276475</td>\n",
       "      <td>8.627800</td>\n",
       "      <td>11.551000</td>\n",
       "      <td>2.182400</td>\n",
       "      <td>2.634100</td>\n",
       "      <td>7.613000</td>\n",
       "      <td>13.456400</td>\n",
       "      <td>-8.321725</td>\n",
       "      <td>-2.307900</td>\n",
       "      <td>4.992100</td>\n",
       "      <td>3.171700</td>\n",
       "      <td>-13.766175</td>\n",
       "      <td>8.870000</td>\n",
       "      <td>-2.500875</td>\n",
       "      <td>11.456300</td>\n",
       "      <td>11.032300</td>\n",
       "      <td>0.116975</td>\n",
       "      <td>-0.007125</td>\n",
       "      <td>4.125475</td>\n",
       "      <td>7.591050</td>\n",
       "      <td>-2.199500</td>\n",
       "      <td>-12.831825</td>\n",
       "      <td>4.519575</td>\n",
       "      <td>10.713200</td>\n",
       "      <td>11.343800</td>\n",
       "      <td>5.313650</td>\n",
       "      <td>-28.730700</td>\n",
       "      <td>9.248750</td>\n",
       "      <td>-20.654525</td>\n",
       "      <td>6.351975</td>\n",
       "      <td>10.653475</td>\n",
       "      <td>12.269000</td>\n",
       "      <td>7.267625</td>\n",
       "      <td>-6.065025</td>\n",
       "      <td>5.435600</td>\n",
       "      <td>-5.147625</td>\n",
       "      <td>8.163900</td>\n",
       "      <td>14.097875</td>\n",
       "      <td>5.687500</td>\n",
       "      <td>0.183500</td>\n",
       "      <td>8.312400</td>\n",
       "      <td>8.912750</td>\n",
       "      <td>-20.901725</td>\n",
       "      <td>-0.572400</td>\n",
       "      <td>-1.588700</td>\n",
       "      <td>5.293500</td>\n",
       "      <td>-1.702800</td>\n",
       "      <td>4.973800</td>\n",
       "      <td>6.753200</td>\n",
       "      <td>5.014000</td>\n",
       "      <td>-6.336625</td>\n",
       "      <td>15.256625</td>\n",
       "      <td>0.472300</td>\n",
       "      <td>-2.197100</td>\n",
       "      <td>14.097275</td>\n",
       "      <td>9.595975</td>\n",
       "      <td>12.480975</td>\n",
       "      <td>0.596300</td>\n",
       "      <td>16.014700</td>\n",
       "      <td>3.817275</td>\n",
       "      <td>13.375400</td>\n",
       "      <td>0.694475</td>\n",
       "      <td>13.214775</td>\n",
       "      <td>-10.004950</td>\n",
       "      <td>-5.106400</td>\n",
       "      <td>-7.216125</td>\n",
       "      <td>15.338575</td>\n",
       "      <td>0.407550</td>\n",
       "      <td>7.247175</td>\n",
       "      <td>6.918775</td>\n",
       "      <td>1.140500</td>\n",
       "      <td>-26.665600</td>\n",
       "      <td>6.869900</td>\n",
       "      <td>9.670300</td>\n",
       "      <td>10.195600</td>\n",
       "      <td>8.828000</td>\n",
       "      <td>-0.527400</td>\n",
       "      <td>7.796950</td>\n",
       "      <td>8.919525</td>\n",
       "      <td>1.267675</td>\n",
       "      <td>-2.106200</td>\n",
       "      <td>-13.198700</td>\n",
       "      <td>9.639800</td>\n",
       "      <td>16.047975</td>\n",
       "      <td>1.428900</td>\n",
       "      <td>10.097900</td>\n",
       "      <td>3.639600</td>\n",
       "      <td>7.282300</td>\n",
       "      <td>12.168075</td>\n",
       "      <td>14.098900</td>\n",
       "      <td>15.107175</td>\n",
       "      <td>2.817475</td>\n",
       "      <td>5.510100</td>\n",
       "      <td>2.092675</td>\n",
       "      <td>4.803250</td>\n",
       "      <td>2.388775</td>\n",
       "      <td>0.399700</td>\n",
       "      <td>1.171875</td>\n",
       "      <td>6.373500</td>\n",
       "      <td>-11.587850</td>\n",
       "      <td>-0.161975</td>\n",
       "      <td>15.696275</td>\n",
       "      <td>9.996400</td>\n",
       "      <td>-2.565200</td>\n",
       "      <td>2.817050</td>\n",
       "      <td>2.353600</td>\n",
       "      <td>12.245400</td>\n",
       "      <td>12.608400</td>\n",
       "      <td>-1.502325</td>\n",
       "      <td>-3.580725</td>\n",
       "      <td>12.514475</td>\n",
       "      <td>11.619300</td>\n",
       "      <td>0.207800</td>\n",
       "      <td>6.724375</td>\n",
       "      <td>6.543500</td>\n",
       "      <td>-9.625700</td>\n",
       "      <td>-9.957100</td>\n",
       "      <td>14.933900</td>\n",
       "      <td>10.656550</td>\n",
       "      <td>-2.011825</td>\n",
       "      <td>2.387575</td>\n",
       "      <td>-0.121700</td>\n",
       "      <td>-2.153725</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>10.311200</td>\n",
       "      <td>7.968075</td>\n",
       "      <td>1.885875</td>\n",
       "      <td>8.646900</td>\n",
       "      <td>-8.751450</td>\n",
       "      <td>3.853600</td>\n",
       "      <td>-1.903200</td>\n",
       "      <td>14.952200</td>\n",
       "      <td>7.064600</td>\n",
       "      <td>5.567900</td>\n",
       "      <td>15.233000</td>\n",
       "      <td>3.339900</td>\n",
       "      <td>-6.266025</td>\n",
       "      <td>12.475100</td>\n",
       "      <td>-8.939950</td>\n",
       "      <td>12.109200</td>\n",
       "      <td>7.243525</td>\n",
       "      <td>15.696125</td>\n",
       "      <td>5.470500</td>\n",
       "      <td>4.326100</td>\n",
       "      <td>7.029600</td>\n",
       "      <td>-7.094025</td>\n",
       "      <td>15.744550</td>\n",
       "      <td>2.699000</td>\n",
       "      <td>-9.643100</td>\n",
       "      <td>2.703200</td>\n",
       "      <td>5.374600</td>\n",
       "      <td>-3.258500</td>\n",
       "      <td>-4.720350</td>\n",
       "      <td>13.731775</td>\n",
       "      <td>-5.009525</td>\n",
       "      <td>15.064600</td>\n",
       "      <td>9.371600</td>\n",
       "      <td>-8.386500</td>\n",
       "      <td>9.808675</td>\n",
       "      <td>-7.395700</td>\n",
       "      <td>0.625575</td>\n",
       "      <td>-6.673900</td>\n",
       "      <td>9.084700</td>\n",
       "      <td>-6.064425</td>\n",
       "      <td>5.423100</td>\n",
       "      <td>5.663300</td>\n",
       "      <td>-7.360000</td>\n",
       "      <td>6.715200</td>\n",
       "      <td>-19.205125</td>\n",
       "      <td>12.501550</td>\n",
       "      <td>0.014900</td>\n",
       "      <td>-0.058825</td>\n",
       "      <td>5.157400</td>\n",
       "      <td>0.889775</td>\n",
       "      <td>0.584600</td>\n",
       "      <td>15.629800</td>\n",
       "      <td>-1.170700</td>\n",
       "      <td>-1.946925</td>\n",
       "      <td>8.252800</td>\n",
       "      <td>13.829700</td>\n",
       "      <td>-11.208475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.524750</td>\n",
       "      <td>-1.608050</td>\n",
       "      <td>10.580000</td>\n",
       "      <td>6.825000</td>\n",
       "      <td>11.108250</td>\n",
       "      <td>-4.833150</td>\n",
       "      <td>5.385100</td>\n",
       "      <td>16.456800</td>\n",
       "      <td>0.393700</td>\n",
       "      <td>7.629600</td>\n",
       "      <td>0.487300</td>\n",
       "      <td>-3.286950</td>\n",
       "      <td>14.025500</td>\n",
       "      <td>8.604250</td>\n",
       "      <td>7.520300</td>\n",
       "      <td>14.574100</td>\n",
       "      <td>9.232050</td>\n",
       "      <td>-5.666350</td>\n",
       "      <td>15.196250</td>\n",
       "      <td>12.453900</td>\n",
       "      <td>13.196800</td>\n",
       "      <td>17.234250</td>\n",
       "      <td>4.275150</td>\n",
       "      <td>3.008650</td>\n",
       "      <td>10.380350</td>\n",
       "      <td>13.662500</td>\n",
       "      <td>-4.196900</td>\n",
       "      <td>-1.132100</td>\n",
       "      <td>5.534850</td>\n",
       "      <td>4.950200</td>\n",
       "      <td>-7.411750</td>\n",
       "      <td>10.365650</td>\n",
       "      <td>-0.497650</td>\n",
       "      <td>14.576000</td>\n",
       "      <td>11.435200</td>\n",
       "      <td>3.917750</td>\n",
       "      <td>2.198000</td>\n",
       "      <td>5.900650</td>\n",
       "      <td>10.562700</td>\n",
       "      <td>0.672300</td>\n",
       "      <td>-6.617450</td>\n",
       "      <td>9.162650</td>\n",
       "      <td>11.243400</td>\n",
       "      <td>11.565000</td>\n",
       "      <td>9.437200</td>\n",
       "      <td>-12.547200</td>\n",
       "      <td>11.310750</td>\n",
       "      <td>-12.482400</td>\n",
       "      <td>14.559200</td>\n",
       "      <td>16.672400</td>\n",
       "      <td>12.745600</td>\n",
       "      <td>13.444400</td>\n",
       "      <td>-2.502450</td>\n",
       "      <td>6.027800</td>\n",
       "      <td>1.274050</td>\n",
       "      <td>12.594100</td>\n",
       "      <td>16.648150</td>\n",
       "      <td>6.262500</td>\n",
       "      <td>3.170100</td>\n",
       "      <td>8.901000</td>\n",
       "      <td>12.064350</td>\n",
       "      <td>-11.892000</td>\n",
       "      <td>0.794700</td>\n",
       "      <td>0.681700</td>\n",
       "      <td>6.377700</td>\n",
       "      <td>1.021350</td>\n",
       "      <td>5.782000</td>\n",
       "      <td>11.922000</td>\n",
       "      <td>5.019100</td>\n",
       "      <td>-3.325500</td>\n",
       "      <td>24.445000</td>\n",
       "      <td>0.668400</td>\n",
       "      <td>0.646450</td>\n",
       "      <td>19.309750</td>\n",
       "      <td>19.536650</td>\n",
       "      <td>16.844200</td>\n",
       "      <td>6.297800</td>\n",
       "      <td>18.967850</td>\n",
       "      <td>5.440050</td>\n",
       "      <td>14.388850</td>\n",
       "      <td>6.061750</td>\n",
       "      <td>14.844500</td>\n",
       "      <td>-3.284450</td>\n",
       "      <td>1.069700</td>\n",
       "      <td>-2.517950</td>\n",
       "      <td>18.296450</td>\n",
       "      <td>6.006700</td>\n",
       "      <td>11.288000</td>\n",
       "      <td>8.616200</td>\n",
       "      <td>3.642550</td>\n",
       "      <td>-16.482600</td>\n",
       "      <td>6.986500</td>\n",
       "      <td>12.673500</td>\n",
       "      <td>10.582200</td>\n",
       "      <td>10.983850</td>\n",
       "      <td>-0.098600</td>\n",
       "      <td>14.369900</td>\n",
       "      <td>18.502150</td>\n",
       "      <td>1.768300</td>\n",
       "      <td>-0.771300</td>\n",
       "      <td>-6.401500</td>\n",
       "      <td>13.380850</td>\n",
       "      <td>22.306850</td>\n",
       "      <td>1.566000</td>\n",
       "      <td>11.497950</td>\n",
       "      <td>4.224500</td>\n",
       "      <td>8.605150</td>\n",
       "      <td>17.573200</td>\n",
       "      <td>14.226600</td>\n",
       "      <td>18.281350</td>\n",
       "      <td>5.394300</td>\n",
       "      <td>6.340100</td>\n",
       "      <td>3.408400</td>\n",
       "      <td>8.148550</td>\n",
       "      <td>3.083800</td>\n",
       "      <td>2.249850</td>\n",
       "      <td>2.456300</td>\n",
       "      <td>15.944850</td>\n",
       "      <td>-5.189500</td>\n",
       "      <td>3.023950</td>\n",
       "      <td>24.354700</td>\n",
       "      <td>11.239700</td>\n",
       "      <td>1.200700</td>\n",
       "      <td>7.234300</td>\n",
       "      <td>4.302100</td>\n",
       "      <td>12.486300</td>\n",
       "      <td>13.166800</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>-1.101750</td>\n",
       "      <td>15.426800</td>\n",
       "      <td>12.264650</td>\n",
       "      <td>0.556600</td>\n",
       "      <td>7.809100</td>\n",
       "      <td>6.806700</td>\n",
       "      <td>-4.704250</td>\n",
       "      <td>-4.111900</td>\n",
       "      <td>22.948300</td>\n",
       "      <td>17.257250</td>\n",
       "      <td>1.211750</td>\n",
       "      <td>8.066250</td>\n",
       "      <td>3.564700</td>\n",
       "      <td>2.975500</td>\n",
       "      <td>11.855900</td>\n",
       "      <td>12.356350</td>\n",
       "      <td>8.651850</td>\n",
       "      <td>4.904700</td>\n",
       "      <td>10.395600</td>\n",
       "      <td>-3.178700</td>\n",
       "      <td>3.996000</td>\n",
       "      <td>5.283250</td>\n",
       "      <td>16.736950</td>\n",
       "      <td>10.127900</td>\n",
       "      <td>7.673700</td>\n",
       "      <td>16.649750</td>\n",
       "      <td>6.994050</td>\n",
       "      <td>-2.066100</td>\n",
       "      <td>13.184300</td>\n",
       "      <td>-4.868400</td>\n",
       "      <td>17.630450</td>\n",
       "      <td>10.217550</td>\n",
       "      <td>23.864500</td>\n",
       "      <td>5.633500</td>\n",
       "      <td>5.359700</td>\n",
       "      <td>10.788700</td>\n",
       "      <td>-2.637800</td>\n",
       "      <td>19.270800</td>\n",
       "      <td>2.960200</td>\n",
       "      <td>-4.011600</td>\n",
       "      <td>4.761600</td>\n",
       "      <td>5.634300</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>-0.807350</td>\n",
       "      <td>19.748000</td>\n",
       "      <td>-0.569750</td>\n",
       "      <td>20.206100</td>\n",
       "      <td>11.679800</td>\n",
       "      <td>-2.538450</td>\n",
       "      <td>11.737250</td>\n",
       "      <td>-0.942050</td>\n",
       "      <td>2.512300</td>\n",
       "      <td>-2.688800</td>\n",
       "      <td>10.036050</td>\n",
       "      <td>0.720200</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>12.521000</td>\n",
       "      <td>-3.946950</td>\n",
       "      <td>8.902150</td>\n",
       "      <td>-10.209750</td>\n",
       "      <td>15.239450</td>\n",
       "      <td>0.742600</td>\n",
       "      <td>3.203600</td>\n",
       "      <td>7.347750</td>\n",
       "      <td>1.901300</td>\n",
       "      <td>3.396350</td>\n",
       "      <td>17.957950</td>\n",
       "      <td>-0.172700</td>\n",
       "      <td>2.408900</td>\n",
       "      <td>8.888200</td>\n",
       "      <td>15.934050</td>\n",
       "      <td>-2.819550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.758200</td>\n",
       "      <td>1.358625</td>\n",
       "      <td>12.516700</td>\n",
       "      <td>8.324100</td>\n",
       "      <td>12.261125</td>\n",
       "      <td>0.924800</td>\n",
       "      <td>6.003000</td>\n",
       "      <td>19.102900</td>\n",
       "      <td>2.937900</td>\n",
       "      <td>8.584425</td>\n",
       "      <td>4.382925</td>\n",
       "      <td>0.852825</td>\n",
       "      <td>14.164200</td>\n",
       "      <td>12.274775</td>\n",
       "      <td>9.270425</td>\n",
       "      <td>14.874500</td>\n",
       "      <td>11.055900</td>\n",
       "      <td>-0.810775</td>\n",
       "      <td>21.013325</td>\n",
       "      <td>18.433300</td>\n",
       "      <td>17.879400</td>\n",
       "      <td>23.089050</td>\n",
       "      <td>6.293200</td>\n",
       "      <td>3.403800</td>\n",
       "      <td>13.479600</td>\n",
       "      <td>13.863700</td>\n",
       "      <td>-0.090200</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>6.093700</td>\n",
       "      <td>6.798925</td>\n",
       "      <td>-1.443450</td>\n",
       "      <td>11.885000</td>\n",
       "      <td>1.469100</td>\n",
       "      <td>18.097125</td>\n",
       "      <td>11.844400</td>\n",
       "      <td>7.487725</td>\n",
       "      <td>4.460400</td>\n",
       "      <td>7.542400</td>\n",
       "      <td>13.598925</td>\n",
       "      <td>3.637825</td>\n",
       "      <td>-0.880875</td>\n",
       "      <td>13.754800</td>\n",
       "      <td>11.756900</td>\n",
       "      <td>11.804600</td>\n",
       "      <td>13.087300</td>\n",
       "      <td>3.150525</td>\n",
       "      <td>13.318300</td>\n",
       "      <td>-4.244525</td>\n",
       "      <td>23.028650</td>\n",
       "      <td>22.549050</td>\n",
       "      <td>13.234500</td>\n",
       "      <td>19.385650</td>\n",
       "      <td>0.944350</td>\n",
       "      <td>6.542900</td>\n",
       "      <td>7.401825</td>\n",
       "      <td>17.086625</td>\n",
       "      <td>19.289700</td>\n",
       "      <td>6.845000</td>\n",
       "      <td>6.209700</td>\n",
       "      <td>9.566525</td>\n",
       "      <td>15.116500</td>\n",
       "      <td>-3.225450</td>\n",
       "      <td>2.228200</td>\n",
       "      <td>3.020300</td>\n",
       "      <td>7.490600</td>\n",
       "      <td>3.739200</td>\n",
       "      <td>6.586200</td>\n",
       "      <td>17.037650</td>\n",
       "      <td>5.024100</td>\n",
       "      <td>-0.498875</td>\n",
       "      <td>33.633150</td>\n",
       "      <td>0.864400</td>\n",
       "      <td>3.510700</td>\n",
       "      <td>25.207125</td>\n",
       "      <td>29.620700</td>\n",
       "      <td>21.432225</td>\n",
       "      <td>11.818800</td>\n",
       "      <td>22.041100</td>\n",
       "      <td>6.867200</td>\n",
       "      <td>15.383100</td>\n",
       "      <td>11.449125</td>\n",
       "      <td>16.340800</td>\n",
       "      <td>3.101725</td>\n",
       "      <td>7.449900</td>\n",
       "      <td>1.986700</td>\n",
       "      <td>21.358850</td>\n",
       "      <td>11.158375</td>\n",
       "      <td>15.433225</td>\n",
       "      <td>10.567025</td>\n",
       "      <td>6.146200</td>\n",
       "      <td>-6.409375</td>\n",
       "      <td>7.101400</td>\n",
       "      <td>15.840225</td>\n",
       "      <td>10.944900</td>\n",
       "      <td>13.089100</td>\n",
       "      <td>0.329100</td>\n",
       "      <td>20.819375</td>\n",
       "      <td>28.158975</td>\n",
       "      <td>2.260900</td>\n",
       "      <td>0.528500</td>\n",
       "      <td>0.132100</td>\n",
       "      <td>17.250225</td>\n",
       "      <td>28.682225</td>\n",
       "      <td>1.705400</td>\n",
       "      <td>12.902100</td>\n",
       "      <td>4.822200</td>\n",
       "      <td>9.928900</td>\n",
       "      <td>23.348600</td>\n",
       "      <td>14.361800</td>\n",
       "      <td>21.852900</td>\n",
       "      <td>8.104325</td>\n",
       "      <td>7.080300</td>\n",
       "      <td>4.577400</td>\n",
       "      <td>11.596200</td>\n",
       "      <td>3.811900</td>\n",
       "      <td>4.121500</td>\n",
       "      <td>3.665100</td>\n",
       "      <td>25.780825</td>\n",
       "      <td>0.971800</td>\n",
       "      <td>6.098400</td>\n",
       "      <td>33.105275</td>\n",
       "      <td>12.619425</td>\n",
       "      <td>5.091700</td>\n",
       "      <td>11.734750</td>\n",
       "      <td>6.192200</td>\n",
       "      <td>12.718100</td>\n",
       "      <td>13.811700</td>\n",
       "      <td>3.293000</td>\n",
       "      <td>1.351700</td>\n",
       "      <td>18.480400</td>\n",
       "      <td>12.876700</td>\n",
       "      <td>0.901000</td>\n",
       "      <td>8.911425</td>\n",
       "      <td>7.070800</td>\n",
       "      <td>-0.178800</td>\n",
       "      <td>1.125950</td>\n",
       "      <td>31.042425</td>\n",
       "      <td>24.426025</td>\n",
       "      <td>4.391225</td>\n",
       "      <td>13.232525</td>\n",
       "      <td>7.078525</td>\n",
       "      <td>8.192425</td>\n",
       "      <td>16.073925</td>\n",
       "      <td>14.461050</td>\n",
       "      <td>9.315000</td>\n",
       "      <td>7.676925</td>\n",
       "      <td>12.113225</td>\n",
       "      <td>2.028275</td>\n",
       "      <td>4.131600</td>\n",
       "      <td>12.688225</td>\n",
       "      <td>18.682500</td>\n",
       "      <td>13.057600</td>\n",
       "      <td>9.817300</td>\n",
       "      <td>18.263900</td>\n",
       "      <td>10.766350</td>\n",
       "      <td>1.891750</td>\n",
       "      <td>13.929300</td>\n",
       "      <td>-0.988575</td>\n",
       "      <td>23.875325</td>\n",
       "      <td>13.094525</td>\n",
       "      <td>32.622850</td>\n",
       "      <td>5.792000</td>\n",
       "      <td>6.371200</td>\n",
       "      <td>14.623900</td>\n",
       "      <td>1.323600</td>\n",
       "      <td>23.024025</td>\n",
       "      <td>3.241500</td>\n",
       "      <td>1.318725</td>\n",
       "      <td>7.020025</td>\n",
       "      <td>5.905400</td>\n",
       "      <td>3.096400</td>\n",
       "      <td>2.956800</td>\n",
       "      <td>25.907725</td>\n",
       "      <td>3.619900</td>\n",
       "      <td>25.641225</td>\n",
       "      <td>13.745500</td>\n",
       "      <td>2.704400</td>\n",
       "      <td>13.931300</td>\n",
       "      <td>5.338750</td>\n",
       "      <td>4.391125</td>\n",
       "      <td>0.996200</td>\n",
       "      <td>11.011300</td>\n",
       "      <td>7.499175</td>\n",
       "      <td>12.127425</td>\n",
       "      <td>19.456150</td>\n",
       "      <td>-0.590650</td>\n",
       "      <td>11.193800</td>\n",
       "      <td>-1.466000</td>\n",
       "      <td>18.345225</td>\n",
       "      <td>1.482900</td>\n",
       "      <td>6.406200</td>\n",
       "      <td>9.512525</td>\n",
       "      <td>2.949500</td>\n",
       "      <td>6.205800</td>\n",
       "      <td>20.396525</td>\n",
       "      <td>0.829600</td>\n",
       "      <td>6.556725</td>\n",
       "      <td>9.593300</td>\n",
       "      <td>18.064725</td>\n",
       "      <td>4.836800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.315000</td>\n",
       "      <td>10.376800</td>\n",
       "      <td>19.353000</td>\n",
       "      <td>13.188300</td>\n",
       "      <td>16.671400</td>\n",
       "      <td>17.251600</td>\n",
       "      <td>8.447700</td>\n",
       "      <td>27.691800</td>\n",
       "      <td>10.151300</td>\n",
       "      <td>11.150600</td>\n",
       "      <td>18.670200</td>\n",
       "      <td>17.188700</td>\n",
       "      <td>14.654500</td>\n",
       "      <td>22.331500</td>\n",
       "      <td>14.937700</td>\n",
       "      <td>15.863300</td>\n",
       "      <td>17.950600</td>\n",
       "      <td>19.025900</td>\n",
       "      <td>41.748000</td>\n",
       "      <td>35.183000</td>\n",
       "      <td>31.285900</td>\n",
       "      <td>49.044300</td>\n",
       "      <td>14.594500</td>\n",
       "      <td>4.875200</td>\n",
       "      <td>25.446000</td>\n",
       "      <td>14.654600</td>\n",
       "      <td>15.675100</td>\n",
       "      <td>3.243100</td>\n",
       "      <td>8.787400</td>\n",
       "      <td>13.143100</td>\n",
       "      <td>15.651500</td>\n",
       "      <td>20.171900</td>\n",
       "      <td>6.787100</td>\n",
       "      <td>29.546600</td>\n",
       "      <td>13.287800</td>\n",
       "      <td>21.528900</td>\n",
       "      <td>14.245600</td>\n",
       "      <td>11.863800</td>\n",
       "      <td>29.823500</td>\n",
       "      <td>15.322300</td>\n",
       "      <td>18.105600</td>\n",
       "      <td>26.165800</td>\n",
       "      <td>13.469600</td>\n",
       "      <td>12.577900</td>\n",
       "      <td>34.196100</td>\n",
       "      <td>62.084400</td>\n",
       "      <td>21.293900</td>\n",
       "      <td>20.685400</td>\n",
       "      <td>54.273800</td>\n",
       "      <td>41.153000</td>\n",
       "      <td>15.317200</td>\n",
       "      <td>40.689000</td>\n",
       "      <td>17.096800</td>\n",
       "      <td>8.231500</td>\n",
       "      <td>28.572400</td>\n",
       "      <td>29.092100</td>\n",
       "      <td>29.074100</td>\n",
       "      <td>9.160900</td>\n",
       "      <td>20.483300</td>\n",
       "      <td>11.986700</td>\n",
       "      <td>25.195500</td>\n",
       "      <td>27.102900</td>\n",
       "      <td>7.753600</td>\n",
       "      <td>11.231700</td>\n",
       "      <td>11.153700</td>\n",
       "      <td>15.731300</td>\n",
       "      <td>9.713200</td>\n",
       "      <td>39.396800</td>\n",
       "      <td>5.046900</td>\n",
       "      <td>8.547300</td>\n",
       "      <td>64.464400</td>\n",
       "      <td>1.571900</td>\n",
       "      <td>14.150000</td>\n",
       "      <td>44.536100</td>\n",
       "      <td>70.272000</td>\n",
       "      <td>36.156700</td>\n",
       "      <td>34.435200</td>\n",
       "      <td>30.956900</td>\n",
       "      <td>11.350700</td>\n",
       "      <td>18.225600</td>\n",
       "      <td>30.476900</td>\n",
       "      <td>23.132400</td>\n",
       "      <td>21.893400</td>\n",
       "      <td>27.714300</td>\n",
       "      <td>17.742400</td>\n",
       "      <td>32.901100</td>\n",
       "      <td>34.563700</td>\n",
       "      <td>33.354100</td>\n",
       "      <td>17.459400</td>\n",
       "      <td>15.481600</td>\n",
       "      <td>27.271300</td>\n",
       "      <td>7.489500</td>\n",
       "      <td>26.997600</td>\n",
       "      <td>12.534300</td>\n",
       "      <td>18.975000</td>\n",
       "      <td>1.804000</td>\n",
       "      <td>40.880600</td>\n",
       "      <td>58.287900</td>\n",
       "      <td>4.502800</td>\n",
       "      <td>5.076400</td>\n",
       "      <td>25.140900</td>\n",
       "      <td>28.459400</td>\n",
       "      <td>51.326500</td>\n",
       "      <td>2.188700</td>\n",
       "      <td>19.020600</td>\n",
       "      <td>7.169200</td>\n",
       "      <td>15.307400</td>\n",
       "      <td>46.379500</td>\n",
       "      <td>14.743000</td>\n",
       "      <td>32.059100</td>\n",
       "      <td>19.519300</td>\n",
       "      <td>9.800200</td>\n",
       "      <td>8.431700</td>\n",
       "      <td>21.542100</td>\n",
       "      <td>6.585000</td>\n",
       "      <td>11.950400</td>\n",
       "      <td>8.120700</td>\n",
       "      <td>64.810900</td>\n",
       "      <td>25.263500</td>\n",
       "      <td>15.688500</td>\n",
       "      <td>74.032100</td>\n",
       "      <td>17.307400</td>\n",
       "      <td>18.471400</td>\n",
       "      <td>26.874900</td>\n",
       "      <td>14.991500</td>\n",
       "      <td>13.664200</td>\n",
       "      <td>15.515600</td>\n",
       "      <td>10.597600</td>\n",
       "      <td>9.809600</td>\n",
       "      <td>31.203600</td>\n",
       "      <td>14.989500</td>\n",
       "      <td>2.192300</td>\n",
       "      <td>12.465000</td>\n",
       "      <td>8.309100</td>\n",
       "      <td>12.723600</td>\n",
       "      <td>21.412800</td>\n",
       "      <td>54.579400</td>\n",
       "      <td>44.437600</td>\n",
       "      <td>18.818700</td>\n",
       "      <td>36.097100</td>\n",
       "      <td>21.121900</td>\n",
       "      <td>23.965800</td>\n",
       "      <td>32.891100</td>\n",
       "      <td>22.691600</td>\n",
       "      <td>11.810100</td>\n",
       "      <td>16.008300</td>\n",
       "      <td>20.437300</td>\n",
       "      <td>22.149400</td>\n",
       "      <td>4.752800</td>\n",
       "      <td>48.424000</td>\n",
       "      <td>25.435700</td>\n",
       "      <td>21.124500</td>\n",
       "      <td>18.384600</td>\n",
       "      <td>24.007500</td>\n",
       "      <td>23.242800</td>\n",
       "      <td>16.831600</td>\n",
       "      <td>16.497000</td>\n",
       "      <td>11.972100</td>\n",
       "      <td>44.779500</td>\n",
       "      <td>25.120000</td>\n",
       "      <td>58.394200</td>\n",
       "      <td>6.309900</td>\n",
       "      <td>10.134400</td>\n",
       "      <td>27.564800</td>\n",
       "      <td>12.119300</td>\n",
       "      <td>38.332200</td>\n",
       "      <td>4.220400</td>\n",
       "      <td>21.276600</td>\n",
       "      <td>14.886100</td>\n",
       "      <td>7.089000</td>\n",
       "      <td>16.731900</td>\n",
       "      <td>17.917300</td>\n",
       "      <td>53.591900</td>\n",
       "      <td>18.855400</td>\n",
       "      <td>43.546800</td>\n",
       "      <td>20.854800</td>\n",
       "      <td>20.245200</td>\n",
       "      <td>20.596500</td>\n",
       "      <td>29.841300</td>\n",
       "      <td>13.448700</td>\n",
       "      <td>12.750500</td>\n",
       "      <td>14.393900</td>\n",
       "      <td>29.248700</td>\n",
       "      <td>23.704900</td>\n",
       "      <td>44.363400</td>\n",
       "      <td>12.997500</td>\n",
       "      <td>21.739200</td>\n",
       "      <td>22.786100</td>\n",
       "      <td>29.330300</td>\n",
       "      <td>4.034100</td>\n",
       "      <td>18.440900</td>\n",
       "      <td>16.716500</td>\n",
       "      <td>8.402400</td>\n",
       "      <td>18.281800</td>\n",
       "      <td>27.928800</td>\n",
       "      <td>4.272900</td>\n",
       "      <td>18.321500</td>\n",
       "      <td>12.000400</td>\n",
       "      <td>26.079100</td>\n",
       "      <td>28.500700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              target          var_0          var_1          var_2  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.100490      10.679914      -1.627622      10.715192   \n",
       "std         0.300653       3.040051       4.050044       2.640894   \n",
       "min         0.000000       0.408400     -15.043400       2.117100   \n",
       "25%         0.000000       8.453850      -4.740025       8.722475   \n",
       "50%         0.000000      10.524750      -1.608050      10.580000   \n",
       "75%         0.000000      12.758200       1.358625      12.516700   \n",
       "max         1.000000      20.315000      10.376800      19.353000   \n",
       "\n",
       "               var_3          var_4          var_5          var_6  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        6.796529      11.078333      -5.065317       5.408949   \n",
       "std         2.043319       1.623150       7.863267       0.866607   \n",
       "min        -0.040200       5.074800     -32.562600       2.347300   \n",
       "25%         5.254075       9.883175     -11.200350       4.767700   \n",
       "50%         6.825000      11.108250      -4.833150       5.385100   \n",
       "75%         8.324100      12.261125       0.924800       6.003000   \n",
       "max        13.188300      16.671400      17.251600       8.447700   \n",
       "\n",
       "               var_7          var_8          var_9         var_10  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       16.545850       0.284162       7.567236       0.394340   \n",
       "std         3.418076       3.332634       1.235070       5.500793   \n",
       "min         5.349700     -10.505500       3.970500     -20.731300   \n",
       "25%        13.943800      -2.317800       6.618800      -3.594950   \n",
       "50%        16.456800       0.393700       7.629600       0.487300   \n",
       "75%        19.102900       2.937900       8.584425       4.382925   \n",
       "max        27.691800      10.151300      11.150600      18.670200   \n",
       "\n",
       "              var_11         var_12         var_13         var_14  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -3.245596      14.023978       8.530232       7.537606   \n",
       "std         5.970253       0.190059       4.639536       2.247908   \n",
       "min       -26.095000      13.434600      -6.011100       1.013300   \n",
       "25%        -7.510600      13.894000       5.072800       5.781875   \n",
       "50%        -3.286950      14.025500       8.604250       7.520300   \n",
       "75%         0.852825      14.164200      12.274775       9.270425   \n",
       "max        17.188700      14.654500      22.331500      14.937700   \n",
       "\n",
       "              var_15         var_16         var_17         var_18  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       14.573126       9.333264      -5.696731      15.244013   \n",
       "std         0.411711       2.557421       6.712612       7.851370   \n",
       "min        13.076900       0.635100     -33.380200     -10.664200   \n",
       "25%        14.262800       7.452275     -10.476225       9.177950   \n",
       "50%        14.574100       9.232050      -5.666350      15.196250   \n",
       "75%        14.874500      11.055900      -0.810775      21.013325   \n",
       "max        15.863300      17.950600      19.025900      41.748000   \n",
       "\n",
       "              var_19         var_20         var_21         var_22  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       12.438567      13.290894      17.257883       4.305430   \n",
       "std         7.996694       5.876254       8.196564       2.847958   \n",
       "min       -12.402500      -5.432200     -10.089000      -5.322500   \n",
       "25%         6.276475       8.627800      11.551000       2.182400   \n",
       "50%        12.453900      13.196800      17.234250       4.275150   \n",
       "75%        18.433300      17.879400      23.089050       6.293200   \n",
       "max        35.183000      31.285900      49.044300      14.594500   \n",
       "\n",
       "              var_23         var_24         var_25         var_26  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        3.019540      10.584400      13.667496      -4.055133   \n",
       "std         0.526893       3.777245       0.285535       5.922210   \n",
       "min         1.209800      -0.678400      12.720000     -24.243100   \n",
       "25%         2.634100       7.613000      13.456400      -8.321725   \n",
       "50%         3.008650      10.380350      13.662500      -4.196900   \n",
       "75%         3.403800      13.479600      13.863700      -0.090200   \n",
       "max         4.875200      25.446000      14.654600      15.675100   \n",
       "\n",
       "              var_27         var_28         var_29         var_30  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -1.137908       5.532980       5.053874      -7.687740   \n",
       "std         1.523714       0.783367       2.615942       7.965198   \n",
       "min        -6.166800       2.089600      -4.787200     -34.798400   \n",
       "25%        -2.307900       4.992100       3.171700     -13.766175   \n",
       "50%        -1.132100       5.534850       4.950200      -7.411750   \n",
       "75%         0.015625       6.093700       6.798925      -1.443450   \n",
       "max         3.243100       8.787400      13.143100      15.651500   \n",
       "\n",
       "              var_31         var_32         var_33         var_34  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       10.393046      -0.512886      14.774147      11.434250   \n",
       "std         2.159891       2.587830       4.322325       0.541614   \n",
       "min         2.140600      -8.986100       1.508500       9.816900   \n",
       "25%         8.870000      -2.500875      11.456300      11.032300   \n",
       "50%        10.365650      -0.497650      14.576000      11.435200   \n",
       "75%        11.885000       1.469100      18.097125      11.844400   \n",
       "max        20.171900       6.787100      29.546600      13.287800   \n",
       "\n",
       "              var_35         var_36         var_37         var_38  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        3.842499       2.187230       5.868899      10.642131   \n",
       "std         5.179559       3.119978       2.249730       4.278903   \n",
       "min       -16.513600      -8.095100      -1.183400      -6.337100   \n",
       "25%         0.116975      -0.007125       4.125475       7.591050   \n",
       "50%         3.917750       2.198000       5.900650      10.562700   \n",
       "75%         7.487725       4.460400       7.542400      13.598925   \n",
       "max        21.528900      14.245600      11.863800      29.823500   \n",
       "\n",
       "              var_39         var_40         var_41         var_42  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.662956      -6.725505       9.299858      11.222356   \n",
       "std         4.068845       8.279259       5.938088       0.695991   \n",
       "min       -14.545700     -35.211700      -8.535900       8.859000   \n",
       "25%        -2.199500     -12.831825       4.519575      10.713200   \n",
       "50%         0.672300      -6.617450       9.162650      11.243400   \n",
       "75%         3.637825      -0.880875      13.754800      11.756900   \n",
       "max        15.322300      18.105600      26.165800      13.469600   \n",
       "\n",
       "              var_43         var_44         var_45         var_46  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       11.569954       8.948289     -12.699667      11.326488   \n",
       "std         0.309599       5.903073      21.404912       2.860511   \n",
       "min        10.652800      -9.939600     -90.252500       1.206200   \n",
       "25%        11.343800       5.313650     -28.730700       9.248750   \n",
       "50%        11.565000       9.437200     -12.547200      11.310750   \n",
       "75%        11.804600      13.087300       3.150525      13.318300   \n",
       "max        12.577900      34.196100      62.084400      21.293900   \n",
       "\n",
       "              var_47         var_48         var_49         var_50  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean      -12.471737      14.704713      16.682499      12.740986   \n",
       "std        10.579862      11.384332       7.855762       0.691709   \n",
       "min       -47.686200     -23.902200      -8.070700      10.385500   \n",
       "25%       -20.654525       6.351975      10.653475      12.269000   \n",
       "50%       -12.482400      14.559200      16.672400      12.745600   \n",
       "75%        -4.244525      23.028650      22.549050      13.234500   \n",
       "max        20.685400      54.273800      41.153000      15.317200   \n",
       "\n",
       "              var_51         var_52         var_53         var_54  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       13.428912      -2.528816       6.008569       1.137117   \n",
       "std         8.187306       4.985532       0.764753       8.414241   \n",
       "min       -15.046200     -24.721400       3.344900     -26.778600   \n",
       "25%         7.267625      -6.065025       5.435600      -5.147625   \n",
       "50%        13.444400      -2.502450       6.027800       1.274050   \n",
       "75%        19.385650       0.944350       6.542900       7.401825   \n",
       "max        40.689000      17.096800       8.231500      28.572400   \n",
       "\n",
       "              var_55         var_56         var_57         var_58  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       12.745852      16.629165       6.272014       3.177633   \n",
       "std         5.690072       3.540174       0.795026       4.296686   \n",
       "min        -3.782600       2.761800       3.442300     -12.600900   \n",
       "25%         8.163900      14.097875       5.687500       0.183500   \n",
       "50%        12.594100      16.648150       6.262500       3.170100   \n",
       "75%        17.086625      19.289700       6.845000       6.209700   \n",
       "max        29.092100      29.074100       9.160900      20.483300   \n",
       "\n",
       "              var_59         var_60         var_61         var_62  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        8.931124      12.155618     -11.946744       0.874170   \n",
       "std         0.854798       4.222389      11.622948       2.026238   \n",
       "min         6.184000      -2.100600     -48.802700      -6.328900   \n",
       "25%         8.312400       8.912750     -20.901725      -0.572400   \n",
       "50%         8.901000      12.064350     -11.892000       0.794700   \n",
       "75%         9.566525      15.116500      -3.225450       2.228200   \n",
       "max        11.986700      25.195500      27.102900       7.753600   \n",
       "\n",
       "              var_63         var_64         var_65         var_66  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.661173       6.369157       0.982891       5.794039   \n",
       "std         3.113089       1.485854       3.786493       1.121366   \n",
       "min       -10.554400       1.611700     -14.088800       1.336800   \n",
       "25%        -1.588700       5.293500      -1.702800       4.973800   \n",
       "50%         0.681700       6.377700       1.021350       5.782000   \n",
       "75%         3.020300       7.490600       3.739200       6.586200   \n",
       "max        11.231700      11.153700      15.731300       9.713200   \n",
       "\n",
       "              var_67         var_68         var_69         var_70  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       11.943223       5.018893      -3.331515      24.446811   \n",
       "std         7.365115       0.007186       3.955723      11.951742   \n",
       "min       -19.544300       4.993800     -16.309400     -17.027500   \n",
       "25%         6.753200       5.014000      -6.336625      15.256625   \n",
       "50%        11.922000       5.019100      -3.325500      24.445000   \n",
       "75%        17.037650       5.024100      -0.498875      33.633150   \n",
       "max        39.396800       5.046900       8.547300      64.464400   \n",
       "\n",
       "              var_71         var_72         var_73         var_74  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.669756       0.640553      19.610888      19.518846   \n",
       "std         0.266696       3.944703       7.466303      14.112591   \n",
       "min        -0.224000     -12.383400      -1.665800     -34.101500   \n",
       "25%         0.472300      -2.197100      14.097275       9.595975   \n",
       "50%         0.668400       0.646450      19.309750      19.536650   \n",
       "75%         0.864400       3.510700      25.207125      29.620700   \n",
       "max         1.571900      14.150000      44.536100      70.272000   \n",
       "\n",
       "              var_75         var_76         var_77         var_78  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       16.853732       6.050871      19.066993       5.349479   \n",
       "std         6.055322       7.938351       3.817292       1.993792   \n",
       "min        -1.293600     -21.633300       7.425700      -1.818300   \n",
       "25%        12.480975       0.596300      16.014700       3.817275   \n",
       "50%        16.844200       6.297800      18.967850       5.440050   \n",
       "75%        21.432225      11.818800      22.041100       6.867200   \n",
       "max        36.156700      34.435200      30.956900      11.350700   \n",
       "\n",
       "              var_79         var_80         var_81         var_82  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       14.402136       5.795044      14.719024      -3.471273   \n",
       "std         1.309055       7.436737       2.299567       8.479255   \n",
       "min        10.445400     -18.042200       7.586500     -30.026600   \n",
       "25%        13.375400       0.694475      13.214775     -10.004950   \n",
       "50%        14.388850       6.061750      14.844500      -3.284450   \n",
       "75%        15.383100      11.449125      16.340800       3.101725   \n",
       "max        18.225600      30.476900      23.132400      21.893400   \n",
       "\n",
       "              var_83         var_84         var_85         var_86  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        1.025817      -2.590209      18.362721       5.621058   \n",
       "std         8.297229       6.225305       3.908536       7.751142   \n",
       "min       -24.220100     -24.439800       7.023000     -19.272200   \n",
       "25%        -5.106400      -7.216125      15.338575       0.407550   \n",
       "50%         1.069700      -2.517950      18.296450       6.006700   \n",
       "75%         7.449900       1.986700      21.358850      11.158375   \n",
       "max        27.714300      17.742400      32.901100      34.563700   \n",
       "\n",
       "              var_87         var_88         var_89         var_90  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       11.351483       8.702924       3.725208     -16.548147   \n",
       "std         5.661867       2.491460       3.560554      13.152810   \n",
       "min        -8.481600       1.350200      -9.601400     -61.718000   \n",
       "25%         7.247175       6.918775       1.140500     -26.665600   \n",
       "50%        11.288000       8.616200       3.642550     -16.482600   \n",
       "75%        15.433225      10.567025       6.146200      -6.409375   \n",
       "max        33.354100      17.459400      15.481600      27.271300   \n",
       "\n",
       "              var_91         var_92         var_93         var_94  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        6.987541      12.739578      10.556740      10.999162   \n",
       "std         0.152641       4.186252       0.543341       2.768099   \n",
       "min         6.521800      -1.018500       8.491600       2.819000   \n",
       "25%         6.869900       9.670300      10.195600       8.828000   \n",
       "50%         6.986500      12.673500      10.582200      10.983850   \n",
       "75%         7.101400      15.840225      10.944900      13.089100   \n",
       "max         7.489500      26.997600      12.534300      18.975000   \n",
       "\n",
       "              var_95         var_96         var_97         var_98  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -0.084344      14.400433      18.539645       1.752012   \n",
       "std         0.621125       8.525400      12.642382       0.715836   \n",
       "min        -2.432400     -12.158400     -21.740000      -0.603500   \n",
       "25%        -0.527400       7.796950       8.919525       1.267675   \n",
       "50%        -0.098600      14.369900      18.502150       1.768300   \n",
       "75%         0.329100      20.819375      28.158975       2.260900   \n",
       "max         1.804000      40.880600      58.287900       4.502800   \n",
       "\n",
       "              var_99        var_100        var_101        var_102  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -0.746296      -6.600518      13.413526      22.294908   \n",
       "std         1.862550       9.181683       4.950537       8.628179   \n",
       "min        -7.280600     -39.179100       0.075700      -7.382900   \n",
       "25%        -2.106200     -13.198700       9.639800      16.047975   \n",
       "50%        -0.771300      -6.401500      13.380850      22.306850   \n",
       "75%         0.528500       0.132100      17.250225      28.682225   \n",
       "max         5.076400      25.140900      28.459400      51.326500   \n",
       "\n",
       "             var_103        var_104        var_105        var_106  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        1.568393      11.509834       4.244744       8.617657   \n",
       "std         0.185020       1.970520       0.855698       1.894899   \n",
       "min         0.979300       4.084600       0.715300       0.942400   \n",
       "25%         1.428900      10.097900       3.639600       7.282300   \n",
       "50%         1.566000      11.497950       4.224500       8.605150   \n",
       "75%         1.705400      12.902100       4.822200       9.928900   \n",
       "max         2.188700      19.020600       7.169200      15.307400   \n",
       "\n",
       "             var_107        var_108        var_109        var_110  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       17.796266      14.224435      18.458001       5.513238   \n",
       "std         7.604723       0.171091       4.355031       3.823253   \n",
       "min        -5.898000      13.729000       5.769700      -9.239800   \n",
       "25%        12.168075      14.098900      15.107175       2.817475   \n",
       "50%        17.573200      14.226600      18.281350       5.394300   \n",
       "75%        23.348600      14.361800      21.852900       8.104325   \n",
       "max        46.379500      14.743000      32.059100      19.519300   \n",
       "\n",
       "             var_111        var_112        var_113        var_114  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        6.312603       3.317843       8.136542       3.081191   \n",
       "std         1.082404       1.591170       4.459077       0.985396   \n",
       "min         2.194200      -2.030200      -5.513900      -0.050500   \n",
       "25%         5.510100       2.092675       4.803250       2.388775   \n",
       "50%         6.340100       3.408400       8.148550       3.083800   \n",
       "75%         7.080300       4.577400      11.596200       3.811900   \n",
       "max         9.800200       8.431700      21.542100       6.585000   \n",
       "\n",
       "             var_115        var_116        var_117        var_118  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        2.213717       2.402570      16.102233      -5.305132   \n",
       "std         2.621851       1.650912      13.297662       8.799268   \n",
       "min        -6.858600      -3.163000     -31.836900     -37.527700   \n",
       "25%         0.399700       1.171875       6.373500     -11.587850   \n",
       "50%         2.249850       2.456300      15.944850      -5.189500   \n",
       "75%         4.121500       3.665100      25.780825       0.971800   \n",
       "max        11.950400       8.120700      64.810900      25.263500   \n",
       "\n",
       "             var_119        var_120        var_121        var_122  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        3.032849      24.521078      11.310591       1.192984   \n",
       "std         4.182796      12.121016       1.714416       5.168479   \n",
       "min        -9.774200     -18.696200       6.305200     -15.194000   \n",
       "25%        -0.161975      15.696275       9.996400      -2.565200   \n",
       "50%         3.023950      24.354700      11.239700       1.200700   \n",
       "75%         6.098400      33.105275      12.619425       5.091700   \n",
       "max        15.688500      74.032100      17.307400      18.471400   \n",
       "\n",
       "             var_123        var_124        var_125        var_126  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        7.076254       4.272740      12.489165      13.202326   \n",
       "std         6.147345       2.736821       0.318100       0.776056   \n",
       "min       -12.405900      -7.053800      11.486100      11.265400   \n",
       "25%         2.817050       2.353600      12.245400      12.608400   \n",
       "50%         7.234300       4.302100      12.486300      13.166800   \n",
       "75%        11.734750       6.192200      12.718100      13.811700   \n",
       "max        26.874900      14.991500      13.664200      15.515600   \n",
       "\n",
       "             var_127        var_128        var_129        var_130  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.851507      -1.127952      15.460314      12.257151   \n",
       "std         3.137684       3.238043       4.136453       0.832199   \n",
       "min        -8.876900     -11.755900       2.186300       9.528300   \n",
       "25%        -1.502325      -3.580725      12.514475      11.619300   \n",
       "50%         0.925000      -1.101750      15.426800      12.264650   \n",
       "75%         3.293000       1.351700      18.480400      12.876700   \n",
       "max        10.597600       9.809600      31.203600      14.989500   \n",
       "\n",
       "             var_131        var_132        var_133        var_134  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.544674       7.799676       6.813270      -4.826053   \n",
       "std         0.456280       1.456486       0.375603       6.166126   \n",
       "min        -0.954800       2.890000       5.359300     -24.254600   \n",
       "25%         0.207800       6.724375       6.543500      -9.625700   \n",
       "50%         0.556600       7.809100       6.806700      -4.704250   \n",
       "75%         0.901000       8.911425       7.070800      -0.178800   \n",
       "max         2.192300      12.465000       8.309100      12.723600   \n",
       "\n",
       "             var_135        var_136        var_137        var_138  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -4.259472      22.968602      17.613651       1.210792   \n",
       "std         7.617732      10.382235       8.890516       4.551750   \n",
       "min       -31.380800      -9.949300      -9.851000     -16.468400   \n",
       "25%        -9.957100      14.933900      10.656550      -2.011825   \n",
       "50%        -4.111900      22.948300      17.257250       1.211750   \n",
       "75%         1.125950      31.042425      24.426025       4.391225   \n",
       "max        21.412800      54.579400      44.437600      18.818700   \n",
       "\n",
       "             var_139        var_140        var_141        var_142  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        7.760193       3.423636       2.897596      11.983489   \n",
       "std         7.686433       4.896325       6.715637       5.691936   \n",
       "min       -21.274300     -15.459500     -16.693700      -7.108000   \n",
       "25%         2.387575      -0.121700      -2.153725       7.900000   \n",
       "50%         8.066250       3.564700       2.975500      11.855900   \n",
       "75%        13.232525       7.078525       8.192425      16.073925   \n",
       "max        36.097100      21.121900      23.965800      32.891100   \n",
       "\n",
       "             var_143        var_144        var_145        var_146  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       12.333698       8.647632       4.841328      10.341178   \n",
       "std         2.934706       0.922469       3.899281       2.518883   \n",
       "min         2.806800       5.444300      -8.273400       0.427400   \n",
       "25%        10.311200       7.968075       1.885875       8.646900   \n",
       "50%        12.356350       8.651850       4.904700      10.395600   \n",
       "75%        14.461050       9.315000       7.676925      12.113225   \n",
       "max        22.691600      11.810100      16.008300      20.437300   \n",
       "\n",
       "             var_147        var_148        var_149        var_150  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -3.300779       3.990726       5.296237      16.817671   \n",
       "std         7.413301       0.199192      10.385133       2.464157   \n",
       "min       -29.984000       3.320500     -41.168300       9.242000   \n",
       "25%        -8.751450       3.853600      -1.903200      14.952200   \n",
       "50%        -3.178700       3.996000       5.283250      16.736950   \n",
       "75%         2.028275       4.131600      12.688225      18.682500   \n",
       "max        22.149400       4.752800      48.424000      25.435700   \n",
       "\n",
       "             var_151        var_152        var_153        var_154  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       10.141542       7.633199      16.727902       6.974955   \n",
       "std         3.962426       3.005373       2.014200       4.961678   \n",
       "min        -2.191500      -2.880000      11.030800      -8.196600   \n",
       "25%         7.064600       5.567900      15.233000       3.339900   \n",
       "50%        10.127900       7.673700      16.649750       6.994050   \n",
       "75%        13.057600       9.817300      18.263900      10.766350   \n",
       "max        21.124500      18.384600      24.007500      23.242800   \n",
       "\n",
       "             var_155        var_156        var_157        var_158  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -2.074128      13.209272      -4.813552      17.914591   \n",
       "std         5.771261       0.955140       5.570272       7.885579   \n",
       "min       -21.840900       9.996500     -22.990400      -4.554400   \n",
       "25%        -6.266025      12.475100      -8.939950      12.109200   \n",
       "50%        -2.066100      13.184300      -4.868400      17.630450   \n",
       "75%         1.891750      13.929300      -0.988575      23.875325   \n",
       "max        16.831600      16.497000      11.972100      44.779500   \n",
       "\n",
       "             var_159        var_160        var_161        var_162  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       10.223282      24.259300       5.633293       5.362896   \n",
       "std         4.122912      10.880263       0.217938       1.419612   \n",
       "min        -4.641600      -7.452200       4.852600       0.623100   \n",
       "25%         7.243525      15.696125       5.470500       4.326100   \n",
       "50%        10.217550      23.864500       5.633500       5.359700   \n",
       "75%        13.094525      32.622850       5.792000       6.371200   \n",
       "max        25.120000      58.394200       6.309900      10.134400   \n",
       "\n",
       "             var_163        var_164        var_165        var_166  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       11.002170      -2.871906      19.315753       2.963335   \n",
       "std         5.262056       5.457784       5.024182       0.369684   \n",
       "min        -6.531700     -19.997700       3.816700       1.851200   \n",
       "25%         7.029600      -7.094025      15.744550       2.699000   \n",
       "50%        10.788700      -2.637800      19.270800       2.960200   \n",
       "75%        14.623900       1.323600      23.024025       3.241500   \n",
       "max        27.564800      12.119300      38.332200       4.220400   \n",
       "\n",
       "             var_167        var_168        var_169        var_170  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -4.151155       4.937124       5.636008      -0.004962   \n",
       "std         7.798020       3.105986       0.369437       4.424621   \n",
       "min       -35.969500      -5.250200       4.258800     -14.506000   \n",
       "25%        -9.643100       2.703200       5.374600      -3.258500   \n",
       "50%        -4.011600       4.761600       5.634300       0.002800   \n",
       "75%         1.318725       7.020025       5.905400       3.096400   \n",
       "max        21.276600      14.886100       7.089000      16.731900   \n",
       "\n",
       "             var_171        var_172        var_173        var_174  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -0.831777      19.817094      -0.677967      20.210677   \n",
       "std         5.378008       8.674171       5.966674       7.136427   \n",
       "min       -22.479300     -11.453300     -22.748700      -2.995300   \n",
       "25%        -4.720350      13.731775      -5.009525      15.064600   \n",
       "50%        -0.807350      19.748000      -0.569750      20.206100   \n",
       "75%         2.956800      25.907725       3.619900      25.641225   \n",
       "max        17.917300      53.591900      18.855400      43.546800   \n",
       "\n",
       "             var_175        var_176        var_177        var_178  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       11.640613      -2.799585      11.882933      -1.014064   \n",
       "std         2.892167       7.513939       2.628895       8.579810   \n",
       "min         3.241500     -29.116500       4.952100     -29.273400   \n",
       "25%         9.371600      -8.386500       9.808675      -7.395700   \n",
       "50%        11.679800      -2.538450      11.737250      -0.942050   \n",
       "75%        13.745500       2.704400      13.931300       5.338750   \n",
       "max        20.854800      20.245200      20.596500      29.841300   \n",
       "\n",
       "             var_179        var_180        var_181        var_182  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        2.591444      -2.741666      10.085518       0.719109   \n",
       "std         2.798956       5.261243       1.371862       8.963434   \n",
       "min        -7.856100     -22.037400       5.416500     -26.001100   \n",
       "25%         0.625575      -6.673900       9.084700      -6.064425   \n",
       "50%         2.512300      -2.688800      10.036050       0.720200   \n",
       "75%         4.391125       0.996200      11.011300       7.499175   \n",
       "max        13.448700      12.750500      14.393900      29.248700   \n",
       "\n",
       "             var_183        var_184        var_185        var_186  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        8.769088      12.756676      -3.983261       8.970274   \n",
       "std         4.474924       9.318280       4.725167       3.189759   \n",
       "min        -4.808200     -18.489700     -22.583300      -3.022300   \n",
       "25%         5.423100       5.663300      -7.360000       6.715200   \n",
       "50%         8.600000      12.521000      -3.946950       8.902150   \n",
       "75%        12.127425      19.456150      -0.590650      11.193800   \n",
       "max        23.704900      44.363400      12.997500      21.739200   \n",
       "\n",
       "             var_187        var_188        var_189        var_190  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean      -10.335043      15.377174       0.746072       3.234440   \n",
       "std        11.574708       3.944604       0.976348       4.559922   \n",
       "min       -47.753600       4.412300      -2.554300     -14.093300   \n",
       "25%       -19.205125      12.501550       0.014900      -0.058825   \n",
       "50%       -10.209750      15.239450       0.742600       3.203600   \n",
       "75%        -1.466000      18.345225       1.482900       6.406200   \n",
       "max        22.786100      29.330300       4.034100      18.440900   \n",
       "\n",
       "             var_191        var_192        var_193        var_194  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        7.438408       1.927839       3.331774      17.993784   \n",
       "std         3.023272       1.478423       3.992030       3.135162   \n",
       "min        -2.691700      -3.814500     -11.783400       8.694400   \n",
       "25%         5.157400       0.889775       0.584600      15.629800   \n",
       "50%         7.347750       1.901300       3.396350      17.957950   \n",
       "75%         9.512525       2.949500       6.205800      20.396525   \n",
       "max        16.716500       8.402400      18.281800      27.928800   \n",
       "\n",
       "             var_195        var_196        var_197        var_198  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean       -0.142088       2.303335       8.908158      15.870720   \n",
       "std         1.429372       5.454369       0.921625       3.010945   \n",
       "min        -5.261000     -14.209600       5.960600       6.299300   \n",
       "25%        -1.170700      -1.946925       8.252800      13.829700   \n",
       "50%        -0.172700       2.408900       8.888200      15.934050   \n",
       "75%         0.829600       6.556725       9.593300      18.064725   \n",
       "max         4.272900      18.321500      12.000400      26.079100   \n",
       "\n",
       "             var_199  \n",
       "count  200000.000000  \n",
       "mean       -3.326537  \n",
       "std        10.438015  \n",
       "min       -38.852800  \n",
       "25%       -11.208475  \n",
       "50%        -2.819550  \n",
       "75%         4.836800  \n",
       "max        28.500700  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400000, 201)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分割特征和标签\n",
    "# 设置SalePrice为标签\n",
    "train_labels = train1['target'].reset_index(drop=True)\n",
    "\n",
    "# 移除SalePrice后的剩余属性设置为特征\n",
    "train_features = train1.drop(['target'], axis=1)\n",
    "test_features = test1\n",
    "# 结合训练和测试的特征，以便将整个数据集在管道中应用\n",
    "all_features = pd.concat([train_features, test_features]).reset_index(drop=True)\n",
    "all_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据丢失的百分比\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('ID_code', 0.0),\n",
       " ('var_0', 0.0),\n",
       " ('var_1', 0.0),\n",
       " ('var_2', 0.0),\n",
       " ('var_3', 0.0),\n",
       " ('var_4', 0.0),\n",
       " ('var_5', 0.0),\n",
       " ('var_6', 0.0),\n",
       " ('var_7', 0.0),\n",
       " ('var_8', 0.0)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 确定缺失值的阈值\n",
    "def percent_missing(df):\n",
    "    # 将传入的数据进行转化为DataFrame对象\n",
    "    data = pd.DataFrame(df)\n",
    "    # 将每一列的列名装在列表里\n",
    "    df_cols = list(pd.DataFrame(data))\n",
    "    dict_x = {}\n",
    "    for i in range(0, len(df_cols)):\n",
    "        # round 可以进行四舍五入，这里我们对每一列的空值进行均值化，算出百分比\n",
    "        dict_x.update({df_cols[i]: round(data[df_cols[i]].isnull().mean()*100,2)})\n",
    "    \n",
    "    return dict_x\n",
    "\n",
    "missing = percent_missing(all_features)\n",
    "df_miss = sorted(missing.items(), key=lambda x: x[1], reverse=True)\n",
    "print('数据丢失的百分比')\n",
    "df_miss[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 并无数据缺失现象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# train_features = train_features['ID_code'].resetdd_index(drop=True)\n",
    "train_features = train_features.drop(['ID_code'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_features = test_features.drop(['ID_code'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 200)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#标准化\n",
    "ss = MinMaxScaler()#分类模型，经常使用的是minmaxscaler归一化，回归模型经常用standardscaler\n",
    "xx_train = ss.fit_transform(train_features, train_labels)\n",
    "xx_test = ss.transform(test_features)\n",
    "xx_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "      <th>128</th>\n",
       "      <th>129</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "      <th>140</th>\n",
       "      <th>141</th>\n",
       "      <th>142</th>\n",
       "      <th>143</th>\n",
       "      <th>144</th>\n",
       "      <th>145</th>\n",
       "      <th>146</th>\n",
       "      <th>147</th>\n",
       "      <th>148</th>\n",
       "      <th>149</th>\n",
       "      <th>150</th>\n",
       "      <th>151</th>\n",
       "      <th>152</th>\n",
       "      <th>153</th>\n",
       "      <th>154</th>\n",
       "      <th>155</th>\n",
       "      <th>156</th>\n",
       "      <th>157</th>\n",
       "      <th>158</th>\n",
       "      <th>159</th>\n",
       "      <th>160</th>\n",
       "      <th>161</th>\n",
       "      <th>162</th>\n",
       "      <th>163</th>\n",
       "      <th>164</th>\n",
       "      <th>165</th>\n",
       "      <th>166</th>\n",
       "      <th>167</th>\n",
       "      <th>168</th>\n",
       "      <th>169</th>\n",
       "      <th>170</th>\n",
       "      <th>171</th>\n",
       "      <th>172</th>\n",
       "      <th>173</th>\n",
       "      <th>174</th>\n",
       "      <th>175</th>\n",
       "      <th>176</th>\n",
       "      <th>177</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.515985</td>\n",
       "      <td>0.527761</td>\n",
       "      <td>0.498848</td>\n",
       "      <td>0.516818</td>\n",
       "      <td>0.517698</td>\n",
       "      <td>0.551997</td>\n",
       "      <td>0.501877</td>\n",
       "      <td>0.501123</td>\n",
       "      <td>0.522330</td>\n",
       "      <td>0.500931</td>\n",
       "      <td>0.536163</td>\n",
       "      <td>0.527899</td>\n",
       "      <td>0.483136</td>\n",
       "      <td>0.513056</td>\n",
       "      <td>0.468552</td>\n",
       "      <td>0.536975</td>\n",
       "      <td>0.502334</td>\n",
       "      <td>0.528249</td>\n",
       "      <td>0.494316</td>\n",
       "      <td>0.522030</td>\n",
       "      <td>0.509915</td>\n",
       "      <td>0.462462</td>\n",
       "      <td>0.483403</td>\n",
       "      <td>0.493736</td>\n",
       "      <td>0.431122</td>\n",
       "      <td>0.489763</td>\n",
       "      <td>0.505733</td>\n",
       "      <td>0.534426</td>\n",
       "      <td>0.514106</td>\n",
       "      <td>0.548852</td>\n",
       "      <td>0.537378</td>\n",
       "      <td>0.457673</td>\n",
       "      <td>0.537191</td>\n",
       "      <td>0.473129</td>\n",
       "      <td>0.465974</td>\n",
       "      <td>0.535088</td>\n",
       "      <td>0.460251</td>\n",
       "      <td>0.540522</td>\n",
       "      <td>0.469551</td>\n",
       "      <td>0.509196</td>\n",
       "      <td>0.534277</td>\n",
       "      <td>0.513974</td>\n",
       "      <td>0.512592</td>\n",
       "      <td>0.476419</td>\n",
       "      <td>0.427950</td>\n",
       "      <td>0.509088</td>\n",
       "      <td>0.503805</td>\n",
       "      <td>0.515045</td>\n",
       "      <td>0.493846</td>\n",
       "      <td>0.502872</td>\n",
       "      <td>0.477621</td>\n",
       "      <td>0.510900</td>\n",
       "      <td>0.530692</td>\n",
       "      <td>0.545097</td>\n",
       "      <td>0.504340</td>\n",
       "      <td>0.502771</td>\n",
       "      <td>0.527030</td>\n",
       "      <td>0.494826</td>\n",
       "      <td>0.476920</td>\n",
       "      <td>0.473422</td>\n",
       "      <td>0.522280</td>\n",
       "      <td>0.485550</td>\n",
       "      <td>0.511491</td>\n",
       "      <td>0.514804</td>\n",
       "      <td>0.498581</td>\n",
       "      <td>0.505421</td>\n",
       "      <td>0.532119</td>\n",
       "      <td>0.534220</td>\n",
       "      <td>0.472570</td>\n",
       "      <td>0.522108</td>\n",
       "      <td>0.508938</td>\n",
       "      <td>0.497665</td>\n",
       "      <td>0.490851</td>\n",
       "      <td>0.460515</td>\n",
       "      <td>0.513735</td>\n",
       "      <td>0.484571</td>\n",
       "      <td>0.493756</td>\n",
       "      <td>0.494717</td>\n",
       "      <td>0.544292</td>\n",
       "      <td>0.508565</td>\n",
       "      <td>0.491296</td>\n",
       "      <td>0.458804</td>\n",
       "      <td>0.511466</td>\n",
       "      <td>0.486112</td>\n",
       "      <td>0.517981</td>\n",
       "      <td>0.438198</td>\n",
       "      <td>0.462391</td>\n",
       "      <td>0.474071</td>\n",
       "      <td>0.456430</td>\n",
       "      <td>0.531300</td>\n",
       "      <td>0.507587</td>\n",
       "      <td>0.481287</td>\n",
       "      <td>0.491078</td>\n",
       "      <td>0.510832</td>\n",
       "      <td>0.506323</td>\n",
       "      <td>0.554257</td>\n",
       "      <td>0.500742</td>\n",
       "      <td>0.503320</td>\n",
       "      <td>0.461295</td>\n",
       "      <td>0.528794</td>\n",
       "      <td>0.506508</td>\n",
       "      <td>0.469911</td>\n",
       "      <td>0.505504</td>\n",
       "      <td>0.487095</td>\n",
       "      <td>0.497137</td>\n",
       "      <td>0.546870</td>\n",
       "      <td>0.534303</td>\n",
       "      <td>0.453240</td>\n",
       "      <td>0.488595</td>\n",
       "      <td>0.482639</td>\n",
       "      <td>0.512987</td>\n",
       "      <td>0.541468</td>\n",
       "      <td>0.511192</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.471960</td>\n",
       "      <td>0.482339</td>\n",
       "      <td>0.493240</td>\n",
       "      <td>0.496019</td>\n",
       "      <td>0.513170</td>\n",
       "      <td>0.502973</td>\n",
       "      <td>0.466064</td>\n",
       "      <td>0.454945</td>\n",
       "      <td>0.486760</td>\n",
       "      <td>0.495971</td>\n",
       "      <td>0.513785</td>\n",
       "      <td>0.460523</td>\n",
       "      <td>0.455726</td>\n",
       "      <td>0.499546</td>\n",
       "      <td>0.492822</td>\n",
       "      <td>0.457452</td>\n",
       "      <td>0.499680</td>\n",
       "      <td>0.476462</td>\n",
       "      <td>0.512760</td>\n",
       "      <td>0.492905</td>\n",
       "      <td>0.525405</td>\n",
       "      <td>0.513724</td>\n",
       "      <td>0.510128</td>\n",
       "      <td>0.505901</td>\n",
       "      <td>0.501010</td>\n",
       "      <td>0.506080</td>\n",
       "      <td>0.516195</td>\n",
       "      <td>0.481838</td>\n",
       "      <td>0.477298</td>\n",
       "      <td>0.479105</td>\n",
       "      <td>0.503210</td>\n",
       "      <td>0.540107</td>\n",
       "      <td>0.495444</td>\n",
       "      <td>0.511826</td>\n",
       "      <td>0.467937</td>\n",
       "      <td>0.518622</td>\n",
       "      <td>0.467816</td>\n",
       "      <td>0.528952</td>\n",
       "      <td>0.494399</td>\n",
       "      <td>0.439025</td>\n",
       "      <td>0.482565</td>\n",
       "      <td>0.511133</td>\n",
       "      <td>0.494235</td>\n",
       "      <td>0.519896</td>\n",
       "      <td>0.455447</td>\n",
       "      <td>0.499465</td>\n",
       "      <td>0.481598</td>\n",
       "      <td>0.535712</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.514243</td>\n",
       "      <td>0.533231</td>\n",
       "      <td>0.449046</td>\n",
       "      <td>0.469414</td>\n",
       "      <td>0.555817</td>\n",
       "      <td>0.505918</td>\n",
       "      <td>0.486612</td>\n",
       "      <td>0.464213</td>\n",
       "      <td>0.535875</td>\n",
       "      <td>0.480749</td>\n",
       "      <td>0.530494</td>\n",
       "      <td>0.498602</td>\n",
       "      <td>0.476862</td>\n",
       "      <td>0.533144</td>\n",
       "      <td>0.443023</td>\n",
       "      <td>0.478042</td>\n",
       "      <td>0.490384</td>\n",
       "      <td>0.554668</td>\n",
       "      <td>0.520086</td>\n",
       "      <td>0.483625</td>\n",
       "      <td>0.476177</td>\n",
       "      <td>0.497133</td>\n",
       "      <td>0.522755</td>\n",
       "      <td>0.484323</td>\n",
       "      <td>0.530461</td>\n",
       "      <td>0.440038</td>\n",
       "      <td>0.500937</td>\n",
       "      <td>0.532601</td>\n",
       "      <td>0.521950</td>\n",
       "      <td>0.470032</td>\n",
       "      <td>0.502746</td>\n",
       "      <td>0.483477</td>\n",
       "      <td>0.536917</td>\n",
       "      <td>0.507605</td>\n",
       "      <td>0.488022</td>\n",
       "      <td>0.483899</td>\n",
       "      <td>0.527460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.152716</td>\n",
       "      <td>0.159324</td>\n",
       "      <td>0.153221</td>\n",
       "      <td>0.154463</td>\n",
       "      <td>0.139968</td>\n",
       "      <td>0.157852</td>\n",
       "      <td>0.142057</td>\n",
       "      <td>0.152988</td>\n",
       "      <td>0.161333</td>\n",
       "      <td>0.172013</td>\n",
       "      <td>0.139609</td>\n",
       "      <td>0.137933</td>\n",
       "      <td>0.155799</td>\n",
       "      <td>0.163695</td>\n",
       "      <td>0.161437</td>\n",
       "      <td>0.147757</td>\n",
       "      <td>0.147695</td>\n",
       "      <td>0.128088</td>\n",
       "      <td>0.149800</td>\n",
       "      <td>0.168049</td>\n",
       "      <td>0.160037</td>\n",
       "      <td>0.138612</td>\n",
       "      <td>0.142991</td>\n",
       "      <td>0.143748</td>\n",
       "      <td>0.144587</td>\n",
       "      <td>0.147594</td>\n",
       "      <td>0.148359</td>\n",
       "      <td>0.161927</td>\n",
       "      <td>0.116959</td>\n",
       "      <td>0.145895</td>\n",
       "      <td>0.157883</td>\n",
       "      <td>0.119786</td>\n",
       "      <td>0.164065</td>\n",
       "      <td>0.154159</td>\n",
       "      <td>0.156044</td>\n",
       "      <td>0.136152</td>\n",
       "      <td>0.139654</td>\n",
       "      <td>0.172430</td>\n",
       "      <td>0.118331</td>\n",
       "      <td>0.136228</td>\n",
       "      <td>0.155283</td>\n",
       "      <td>0.171118</td>\n",
       "      <td>0.150955</td>\n",
       "      <td>0.160823</td>\n",
       "      <td>0.133748</td>\n",
       "      <td>0.140510</td>\n",
       "      <td>0.142401</td>\n",
       "      <td>0.154741</td>\n",
       "      <td>0.145624</td>\n",
       "      <td>0.159593</td>\n",
       "      <td>0.140258</td>\n",
       "      <td>0.146897</td>\n",
       "      <td>0.119219</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.152016</td>\n",
       "      <td>0.173084</td>\n",
       "      <td>0.134544</td>\n",
       "      <td>0.139025</td>\n",
       "      <td>0.129871</td>\n",
       "      <td>0.147310</td>\n",
       "      <td>0.154688</td>\n",
       "      <td>0.153124</td>\n",
       "      <td>0.143883</td>\n",
       "      <td>0.142893</td>\n",
       "      <td>0.155717</td>\n",
       "      <td>0.126978</td>\n",
       "      <td>0.133872</td>\n",
       "      <td>0.124957</td>\n",
       "      <td>0.135335</td>\n",
       "      <td>0.159141</td>\n",
       "      <td>0.146662</td>\n",
       "      <td>0.148503</td>\n",
       "      <td>0.148669</td>\n",
       "      <td>0.161602</td>\n",
       "      <td>0.135212</td>\n",
       "      <td>0.161690</td>\n",
       "      <td>0.141583</td>\n",
       "      <td>0.162223</td>\n",
       "      <td>0.151400</td>\n",
       "      <td>0.168255</td>\n",
       "      <td>0.153274</td>\n",
       "      <td>0.147921</td>\n",
       "      <td>0.163314</td>\n",
       "      <td>0.159764</td>\n",
       "      <td>0.147581</td>\n",
       "      <td>0.151036</td>\n",
       "      <td>0.143977</td>\n",
       "      <td>0.135336</td>\n",
       "      <td>0.154661</td>\n",
       "      <td>0.141951</td>\n",
       "      <td>0.147802</td>\n",
       "      <td>0.157736</td>\n",
       "      <td>0.149423</td>\n",
       "      <td>0.134401</td>\n",
       "      <td>0.171336</td>\n",
       "      <td>0.146616</td>\n",
       "      <td>0.160738</td>\n",
       "      <td>0.157975</td>\n",
       "      <td>0.140187</td>\n",
       "      <td>0.150728</td>\n",
       "      <td>0.142750</td>\n",
       "      <td>0.174415</td>\n",
       "      <td>0.146964</td>\n",
       "      <td>0.152985</td>\n",
       "      <td>0.131931</td>\n",
       "      <td>0.132586</td>\n",
       "      <td>0.131911</td>\n",
       "      <td>0.145468</td>\n",
       "      <td>0.168729</td>\n",
       "      <td>0.165657</td>\n",
       "      <td>0.132941</td>\n",
       "      <td>0.142309</td>\n",
       "      <td>0.152092</td>\n",
       "      <td>0.164809</td>\n",
       "      <td>0.148504</td>\n",
       "      <td>0.139393</td>\n",
       "      <td>0.146309</td>\n",
       "      <td>0.137589</td>\n",
       "      <td>0.140135</td>\n",
       "      <td>0.164271</td>\n",
       "      <td>0.130715</td>\n",
       "      <td>0.155825</td>\n",
       "      <td>0.153525</td>\n",
       "      <td>0.156497</td>\n",
       "      <td>0.124145</td>\n",
       "      <td>0.146045</td>\n",
       "      <td>0.182593</td>\n",
       "      <td>0.161118</td>\n",
       "      <td>0.150149</td>\n",
       "      <td>0.142551</td>\n",
       "      <td>0.152384</td>\n",
       "      <td>0.144984</td>\n",
       "      <td>0.152113</td>\n",
       "      <td>0.127332</td>\n",
       "      <td>0.166750</td>\n",
       "      <td>0.144293</td>\n",
       "      <td>0.160893</td>\n",
       "      <td>0.163764</td>\n",
       "      <td>0.128992</td>\n",
       "      <td>0.133977</td>\n",
       "      <td>0.133847</td>\n",
       "      <td>0.165168</td>\n",
       "      <td>0.142302</td>\n",
       "      <td>0.147585</td>\n",
       "      <td>0.144910</td>\n",
       "      <td>0.160585</td>\n",
       "      <td>0.125882</td>\n",
       "      <td>0.142199</td>\n",
       "      <td>0.139072</td>\n",
       "      <td>0.115915</td>\n",
       "      <td>0.152168</td>\n",
       "      <td>0.169944</td>\n",
       "      <td>0.141332</td>\n",
       "      <td>0.155217</td>\n",
       "      <td>0.157817</td>\n",
       "      <td>0.149234</td>\n",
       "      <td>0.146933</td>\n",
       "      <td>0.159321</td>\n",
       "      <td>0.159841</td>\n",
       "      <td>0.138531</td>\n",
       "      <td>0.165237</td>\n",
       "      <td>0.149549</td>\n",
       "      <td>0.149255</td>\n",
       "      <td>0.154328</td>\n",
       "      <td>0.169934</td>\n",
       "      <td>0.145563</td>\n",
       "      <td>0.156038</td>\n",
       "      <td>0.136219</td>\n",
       "      <td>0.154248</td>\n",
       "      <td>0.130534</td>\n",
       "      <td>0.141643</td>\n",
       "      <td>0.133130</td>\n",
       "      <td>0.133356</td>\n",
       "      <td>0.143416</td>\n",
       "      <td>0.153333</td>\n",
       "      <td>0.164204</td>\n",
       "      <td>0.152222</td>\n",
       "      <td>0.168041</td>\n",
       "      <td>0.145138</td>\n",
       "      <td>0.131377</td>\n",
       "      <td>0.151238</td>\n",
       "      <td>0.152813</td>\n",
       "      <td>0.162235</td>\n",
       "      <td>0.156943</td>\n",
       "      <td>0.148255</td>\n",
       "      <td>0.132801</td>\n",
       "      <td>0.128819</td>\n",
       "      <td>0.164088</td>\n",
       "      <td>0.158303</td>\n",
       "      <td>0.148192</td>\n",
       "      <td>0.140158</td>\n",
       "      <td>0.155773</td>\n",
       "      <td>0.121015</td>\n",
       "      <td>0.132779</td>\n",
       "      <td>0.162998</td>\n",
       "      <td>0.149925</td>\n",
       "      <td>0.167666</td>\n",
       "      <td>0.152592</td>\n",
       "      <td>0.152223</td>\n",
       "      <td>0.154974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.404160</td>\n",
       "      <td>0.405322</td>\n",
       "      <td>0.383234</td>\n",
       "      <td>0.400217</td>\n",
       "      <td>0.414637</td>\n",
       "      <td>0.428839</td>\n",
       "      <td>0.396761</td>\n",
       "      <td>0.384659</td>\n",
       "      <td>0.396368</td>\n",
       "      <td>0.368839</td>\n",
       "      <td>0.434916</td>\n",
       "      <td>0.429363</td>\n",
       "      <td>0.376588</td>\n",
       "      <td>0.391069</td>\n",
       "      <td>0.342462</td>\n",
       "      <td>0.425603</td>\n",
       "      <td>0.393704</td>\n",
       "      <td>0.437048</td>\n",
       "      <td>0.378579</td>\n",
       "      <td>0.392535</td>\n",
       "      <td>0.382917</td>\n",
       "      <td>0.365953</td>\n",
       "      <td>0.376809</td>\n",
       "      <td>0.388580</td>\n",
       "      <td>0.317381</td>\n",
       "      <td>0.380647</td>\n",
       "      <td>0.398850</td>\n",
       "      <td>0.410089</td>\n",
       "      <td>0.433351</td>\n",
       "      <td>0.443880</td>\n",
       "      <td>0.416893</td>\n",
       "      <td>0.373207</td>\n",
       "      <td>0.411155</td>\n",
       "      <td>0.354796</td>\n",
       "      <td>0.350169</td>\n",
       "      <td>0.437158</td>\n",
       "      <td>0.362029</td>\n",
       "      <td>0.406898</td>\n",
       "      <td>0.385175</td>\n",
       "      <td>0.413359</td>\n",
       "      <td>0.419749</td>\n",
       "      <td>0.376220</td>\n",
       "      <td>0.402160</td>\n",
       "      <td>0.358942</td>\n",
       "      <td>0.345599</td>\n",
       "      <td>0.403854</td>\n",
       "      <td>0.400372</td>\n",
       "      <td>0.395364</td>\n",
       "      <td>0.387001</td>\n",
       "      <td>0.380389</td>\n",
       "      <td>0.381917</td>\n",
       "      <td>0.400354</td>\n",
       "      <td>0.446131</td>\n",
       "      <td>0.427843</td>\n",
       "      <td>0.390796</td>\n",
       "      <td>0.363395</td>\n",
       "      <td>0.430828</td>\n",
       "      <td>0.392614</td>\n",
       "      <td>0.386420</td>\n",
       "      <td>0.366795</td>\n",
       "      <td>0.403477</td>\n",
       "      <td>0.367575</td>\n",
       "      <td>0.408770</td>\n",
       "      <td>0.411533</td>\n",
       "      <td>0.385852</td>\n",
       "      <td>0.415357</td>\n",
       "      <td>0.434196</td>\n",
       "      <td>0.446166</td>\n",
       "      <td>0.380414</td>\n",
       "      <td>0.401211</td>\n",
       "      <td>0.396164</td>\n",
       "      <td>0.387716</td>\n",
       "      <td>0.383905</td>\n",
       "      <td>0.341178</td>\n",
       "      <td>0.418664</td>\n",
       "      <td>0.367809</td>\n",
       "      <td>0.396472</td>\n",
       "      <td>0.365005</td>\n",
       "      <td>0.427943</td>\n",
       "      <td>0.376597</td>\n",
       "      <td>0.386171</td>\n",
       "      <td>0.362042</td>\n",
       "      <td>0.385625</td>\n",
       "      <td>0.368035</td>\n",
       "      <td>0.408316</td>\n",
       "      <td>0.321336</td>\n",
       "      <td>0.365551</td>\n",
       "      <td>0.375965</td>\n",
       "      <td>0.345677</td>\n",
       "      <td>0.428254</td>\n",
       "      <td>0.393895</td>\n",
       "      <td>0.359719</td>\n",
       "      <td>0.381523</td>\n",
       "      <td>0.421500</td>\n",
       "      <td>0.371936</td>\n",
       "      <td>0.449674</td>\n",
       "      <td>0.376239</td>\n",
       "      <td>0.383110</td>\n",
       "      <td>0.366444</td>\n",
       "      <td>0.418742</td>\n",
       "      <td>0.403924</td>\n",
       "      <td>0.336957</td>\n",
       "      <td>0.399099</td>\n",
       "      <td>0.371755</td>\n",
       "      <td>0.402604</td>\n",
       "      <td>0.453106</td>\n",
       "      <td>0.441344</td>\n",
       "      <td>0.345580</td>\n",
       "      <td>0.364793</td>\n",
       "      <td>0.355180</td>\n",
       "      <td>0.419251</td>\n",
       "      <td>0.435958</td>\n",
       "      <td>0.394085</td>\n",
       "      <td>0.381326</td>\n",
       "      <td>0.367610</td>\n",
       "      <td>0.385895</td>\n",
       "      <td>0.384171</td>\n",
       "      <td>0.395357</td>\n",
       "      <td>0.413113</td>\n",
       "      <td>0.377502</td>\n",
       "      <td>0.370895</td>\n",
       "      <td>0.335497</td>\n",
       "      <td>0.375127</td>\n",
       "      <td>0.387542</td>\n",
       "      <td>0.426730</td>\n",
       "      <td>0.348607</td>\n",
       "      <td>0.315985</td>\n",
       "      <td>0.378679</td>\n",
       "      <td>0.379086</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>0.382883</td>\n",
       "      <td>0.369419</td>\n",
       "      <td>0.400457</td>\n",
       "      <td>0.401451</td>\n",
       "      <td>0.395609</td>\n",
       "      <td>0.405801</td>\n",
       "      <td>0.385614</td>\n",
       "      <td>0.377751</td>\n",
       "      <td>0.409684</td>\n",
       "      <td>0.412433</td>\n",
       "      <td>0.419279</td>\n",
       "      <td>0.357603</td>\n",
       "      <td>0.375208</td>\n",
       "      <td>0.377394</td>\n",
       "      <td>0.396458</td>\n",
       "      <td>0.418392</td>\n",
       "      <td>0.410772</td>\n",
       "      <td>0.407273</td>\n",
       "      <td>0.372199</td>\n",
       "      <td>0.438264</td>\n",
       "      <td>0.352619</td>\n",
       "      <td>0.396985</td>\n",
       "      <td>0.397275</td>\n",
       "      <td>0.323827</td>\n",
       "      <td>0.366944</td>\n",
       "      <td>0.402738</td>\n",
       "      <td>0.381294</td>\n",
       "      <td>0.401872</td>\n",
       "      <td>0.337772</td>\n",
       "      <td>0.399344</td>\n",
       "      <td>0.351550</td>\n",
       "      <td>0.424003</td>\n",
       "      <td>0.389326</td>\n",
       "      <td>0.397733</td>\n",
       "      <td>0.401771</td>\n",
       "      <td>0.345580</td>\n",
       "      <td>0.357842</td>\n",
       "      <td>0.459881</td>\n",
       "      <td>0.394978</td>\n",
       "      <td>0.394248</td>\n",
       "      <td>0.360059</td>\n",
       "      <td>0.439615</td>\n",
       "      <td>0.387193</td>\n",
       "      <td>0.426380</td>\n",
       "      <td>0.388034</td>\n",
       "      <td>0.348038</td>\n",
       "      <td>0.419961</td>\n",
       "      <td>0.310435</td>\n",
       "      <td>0.370089</td>\n",
       "      <td>0.398111</td>\n",
       "      <td>0.441633</td>\n",
       "      <td>0.408604</td>\n",
       "      <td>0.360846</td>\n",
       "      <td>0.358828</td>\n",
       "      <td>0.384277</td>\n",
       "      <td>0.427852</td>\n",
       "      <td>0.393252</td>\n",
       "      <td>0.404715</td>\n",
       "      <td>0.324635</td>\n",
       "      <td>0.389958</td>\n",
       "      <td>0.431376</td>\n",
       "      <td>0.404422</td>\n",
       "      <td>0.385063</td>\n",
       "      <td>0.411373</td>\n",
       "      <td>0.360573</td>\n",
       "      <td>0.429027</td>\n",
       "      <td>0.376952</td>\n",
       "      <td>0.379516</td>\n",
       "      <td>0.380712</td>\n",
       "      <td>0.410436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.508191</td>\n",
       "      <td>0.528530</td>\n",
       "      <td>0.491004</td>\n",
       "      <td>0.518970</td>\n",
       "      <td>0.520277</td>\n",
       "      <td>0.556658</td>\n",
       "      <td>0.497967</td>\n",
       "      <td>0.497138</td>\n",
       "      <td>0.527633</td>\n",
       "      <td>0.509617</td>\n",
       "      <td>0.538523</td>\n",
       "      <td>0.526943</td>\n",
       "      <td>0.484384</td>\n",
       "      <td>0.515667</td>\n",
       "      <td>0.467309</td>\n",
       "      <td>0.537324</td>\n",
       "      <td>0.496489</td>\n",
       "      <td>0.528829</td>\n",
       "      <td>0.493405</td>\n",
       "      <td>0.522352</td>\n",
       "      <td>0.507352</td>\n",
       "      <td>0.462062</td>\n",
       "      <td>0.481882</td>\n",
       "      <td>0.490765</td>\n",
       "      <td>0.423311</td>\n",
       "      <td>0.487181</td>\n",
       "      <td>0.502182</td>\n",
       "      <td>0.535043</td>\n",
       "      <td>0.514385</td>\n",
       "      <td>0.543070</td>\n",
       "      <td>0.542848</td>\n",
       "      <td>0.456154</td>\n",
       "      <td>0.538156</td>\n",
       "      <td>0.466062</td>\n",
       "      <td>0.466248</td>\n",
       "      <td>0.537066</td>\n",
       "      <td>0.460733</td>\n",
       "      <td>0.542956</td>\n",
       "      <td>0.467354</td>\n",
       "      <td>0.509509</td>\n",
       "      <td>0.536303</td>\n",
       "      <td>0.510020</td>\n",
       "      <td>0.517156</td>\n",
       "      <td>0.473846</td>\n",
       "      <td>0.439028</td>\n",
       "      <td>0.510088</td>\n",
       "      <td>0.503022</td>\n",
       "      <td>0.514889</td>\n",
       "      <td>0.491985</td>\n",
       "      <td>0.502666</td>\n",
       "      <td>0.478557</td>\n",
       "      <td>0.511178</td>\n",
       "      <td>0.531322</td>\n",
       "      <td>0.549032</td>\n",
       "      <td>0.506814</td>\n",
       "      <td>0.498155</td>\n",
       "      <td>0.527751</td>\n",
       "      <td>0.493163</td>\n",
       "      <td>0.476693</td>\n",
       "      <td>0.468230</td>\n",
       "      <td>0.518937</td>\n",
       "      <td>0.486271</td>\n",
       "      <td>0.505848</td>\n",
       "      <td>0.515746</td>\n",
       "      <td>0.499476</td>\n",
       "      <td>0.506710</td>\n",
       "      <td>0.530681</td>\n",
       "      <td>0.533860</td>\n",
       "      <td>0.476460</td>\n",
       "      <td>0.522350</td>\n",
       "      <td>0.508916</td>\n",
       "      <td>0.496910</td>\n",
       "      <td>0.491074</td>\n",
       "      <td>0.453998</td>\n",
       "      <td>0.513906</td>\n",
       "      <td>0.484317</td>\n",
       "      <td>0.498160</td>\n",
       "      <td>0.490504</td>\n",
       "      <td>0.551169</td>\n",
       "      <td>0.506857</td>\n",
       "      <td>0.496793</td>\n",
       "      <td>0.466876</td>\n",
       "      <td>0.515065</td>\n",
       "      <td>0.486957</td>\n",
       "      <td>0.519694</td>\n",
       "      <td>0.435637</td>\n",
       "      <td>0.469555</td>\n",
       "      <td>0.472553</td>\n",
       "      <td>0.451047</td>\n",
       "      <td>0.528005</td>\n",
       "      <td>0.508324</td>\n",
       "      <td>0.480211</td>\n",
       "      <td>0.488719</td>\n",
       "      <td>0.517130</td>\n",
       "      <td>0.505376</td>\n",
       "      <td>0.550892</td>\n",
       "      <td>0.500166</td>\n",
       "      <td>0.502852</td>\n",
       "      <td>0.464485</td>\n",
       "      <td>0.526770</td>\n",
       "      <td>0.509602</td>\n",
       "      <td>0.468760</td>\n",
       "      <td>0.505707</td>\n",
       "      <td>0.485117</td>\n",
       "      <td>0.496341</td>\n",
       "      <td>0.543733</td>\n",
       "      <td>0.533432</td>\n",
       "      <td>0.448973</td>\n",
       "      <td>0.490730</td>\n",
       "      <td>0.475920</td>\n",
       "      <td>0.508851</td>\n",
       "      <td>0.545083</td>\n",
       "      <td>0.519848</td>\n",
       "      <td>0.504969</td>\n",
       "      <td>0.472353</td>\n",
       "      <td>0.484260</td>\n",
       "      <td>0.498002</td>\n",
       "      <td>0.494390</td>\n",
       "      <td>0.515012</td>\n",
       "      <td>0.502623</td>\n",
       "      <td>0.464269</td>\n",
       "      <td>0.448501</td>\n",
       "      <td>0.486990</td>\n",
       "      <td>0.499995</td>\n",
       "      <td>0.515117</td>\n",
       "      <td>0.459208</td>\n",
       "      <td>0.447367</td>\n",
       "      <td>0.503320</td>\n",
       "      <td>0.494037</td>\n",
       "      <td>0.456297</td>\n",
       "      <td>0.501053</td>\n",
       "      <td>0.480252</td>\n",
       "      <td>0.513744</td>\n",
       "      <td>0.490677</td>\n",
       "      <td>0.528699</td>\n",
       "      <td>0.516519</td>\n",
       "      <td>0.509813</td>\n",
       "      <td>0.499336</td>\n",
       "      <td>0.501037</td>\n",
       "      <td>0.511414</td>\n",
       "      <td>0.520051</td>\n",
       "      <td>0.483754</td>\n",
       "      <td>0.474108</td>\n",
       "      <td>0.480244</td>\n",
       "      <td>0.503872</td>\n",
       "      <td>0.542717</td>\n",
       "      <td>0.498163</td>\n",
       "      <td>0.514168</td>\n",
       "      <td>0.471619</td>\n",
       "      <td>0.518477</td>\n",
       "      <td>0.462831</td>\n",
       "      <td>0.528367</td>\n",
       "      <td>0.496304</td>\n",
       "      <td>0.433003</td>\n",
       "      <td>0.483172</td>\n",
       "      <td>0.511340</td>\n",
       "      <td>0.490393</td>\n",
       "      <td>0.518327</td>\n",
       "      <td>0.449688</td>\n",
       "      <td>0.499273</td>\n",
       "      <td>0.475602</td>\n",
       "      <td>0.535854</td>\n",
       "      <td>0.497997</td>\n",
       "      <td>0.507982</td>\n",
       "      <td>0.540521</td>\n",
       "      <td>0.447744</td>\n",
       "      <td>0.468090</td>\n",
       "      <td>0.558255</td>\n",
       "      <td>0.497202</td>\n",
       "      <td>0.486008</td>\n",
       "      <td>0.464461</td>\n",
       "      <td>0.536480</td>\n",
       "      <td>0.479686</td>\n",
       "      <td>0.533095</td>\n",
       "      <td>0.498504</td>\n",
       "      <td>0.479087</td>\n",
       "      <td>0.538435</td>\n",
       "      <td>0.433711</td>\n",
       "      <td>0.479261</td>\n",
       "      <td>0.486670</td>\n",
       "      <td>0.556188</td>\n",
       "      <td>0.514575</td>\n",
       "      <td>0.483645</td>\n",
       "      <td>0.470247</td>\n",
       "      <td>0.493384</td>\n",
       "      <td>0.523775</td>\n",
       "      <td>0.481572</td>\n",
       "      <td>0.532237</td>\n",
       "      <td>0.434511</td>\n",
       "      <td>0.500410</td>\n",
       "      <td>0.531653</td>\n",
       "      <td>0.517279</td>\n",
       "      <td>0.467860</td>\n",
       "      <td>0.504894</td>\n",
       "      <td>0.481614</td>\n",
       "      <td>0.533706</td>\n",
       "      <td>0.510850</td>\n",
       "      <td>0.484718</td>\n",
       "      <td>0.487100</td>\n",
       "      <td>0.534987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.620387</td>\n",
       "      <td>0.645236</td>\n",
       "      <td>0.603369</td>\n",
       "      <td>0.632294</td>\n",
       "      <td>0.619692</td>\n",
       "      <td>0.672246</td>\n",
       "      <td>0.599256</td>\n",
       "      <td>0.615573</td>\n",
       "      <td>0.650798</td>\n",
       "      <td>0.642599</td>\n",
       "      <td>0.637393</td>\n",
       "      <td>0.622586</td>\n",
       "      <td>0.598082</td>\n",
       "      <td>0.645173</td>\n",
       "      <td>0.592997</td>\n",
       "      <td>0.645134</td>\n",
       "      <td>0.601819</td>\n",
       "      <td>0.621482</td>\n",
       "      <td>0.604392</td>\n",
       "      <td>0.648008</td>\n",
       "      <td>0.634880</td>\n",
       "      <td>0.561072</td>\n",
       "      <td>0.583205</td>\n",
       "      <td>0.598570</td>\n",
       "      <td>0.541945</td>\n",
       "      <td>0.591182</td>\n",
       "      <td>0.605060</td>\n",
       "      <td>0.657013</td>\n",
       "      <td>0.597823</td>\n",
       "      <td>0.646176</td>\n",
       "      <td>0.661150</td>\n",
       "      <td>0.540416</td>\n",
       "      <td>0.662846</td>\n",
       "      <td>0.591646</td>\n",
       "      <td>0.584142</td>\n",
       "      <td>0.630908</td>\n",
       "      <td>0.562001</td>\n",
       "      <td>0.668787</td>\n",
       "      <td>0.551319</td>\n",
       "      <td>0.608796</td>\n",
       "      <td>0.643897</td>\n",
       "      <td>0.642352</td>\n",
       "      <td>0.628530</td>\n",
       "      <td>0.598307</td>\n",
       "      <td>0.521730</td>\n",
       "      <td>0.613135</td>\n",
       "      <td>0.602961</td>\n",
       "      <td>0.635376</td>\n",
       "      <td>0.600323</td>\n",
       "      <td>0.622053</td>\n",
       "      <td>0.577691</td>\n",
       "      <td>0.617776</td>\n",
       "      <td>0.613746</td>\n",
       "      <td>0.654443</td>\n",
       "      <td>0.617521</td>\n",
       "      <td>0.634811</td>\n",
       "      <td>0.628143</td>\n",
       "      <td>0.595023</td>\n",
       "      <td>0.568567</td>\n",
       "      <td>0.582923</td>\n",
       "      <td>0.630753</td>\n",
       "      <td>0.600446</td>\n",
       "      <td>0.607641</td>\n",
       "      <td>0.623090</td>\n",
       "      <td>0.616108</td>\n",
       "      <td>0.597852</td>\n",
       "      <td>0.626689</td>\n",
       "      <td>0.620653</td>\n",
       "      <td>0.570621</td>\n",
       "      <td>0.636067</td>\n",
       "      <td>0.621665</td>\n",
       "      <td>0.606047</td>\n",
       "      <td>0.599022</td>\n",
       "      <td>0.581641</td>\n",
       "      <td>0.610521</td>\n",
       "      <td>0.606826</td>\n",
       "      <td>0.596629</td>\n",
       "      <td>0.621107</td>\n",
       "      <td>0.659541</td>\n",
       "      <td>0.634649</td>\n",
       "      <td>0.607829</td>\n",
       "      <td>0.563126</td>\n",
       "      <td>0.638065</td>\n",
       "      <td>0.609808</td>\n",
       "      <td>0.626485</td>\n",
       "      <td>0.553976</td>\n",
       "      <td>0.565247</td>\n",
       "      <td>0.571637</td>\n",
       "      <td>0.572147</td>\n",
       "      <td>0.627820</td>\n",
       "      <td>0.621520</td>\n",
       "      <td>0.598946</td>\n",
       "      <td>0.601751</td>\n",
       "      <td>0.606847</td>\n",
       "      <td>0.635683</td>\n",
       "      <td>0.651851</td>\n",
       "      <td>0.621765</td>\n",
       "      <td>0.623520</td>\n",
       "      <td>0.560954</td>\n",
       "      <td>0.631958</td>\n",
       "      <td>0.611182</td>\n",
       "      <td>0.605084</td>\n",
       "      <td>0.614299</td>\n",
       "      <td>0.600380</td>\n",
       "      <td>0.590352</td>\n",
       "      <td>0.636344</td>\n",
       "      <td>0.625583</td>\n",
       "      <td>0.559449</td>\n",
       "      <td>0.624063</td>\n",
       "      <td>0.611775</td>\n",
       "      <td>0.603083</td>\n",
       "      <td>0.642401</td>\n",
       "      <td>0.631587</td>\n",
       "      <td>0.632396</td>\n",
       "      <td>0.582081</td>\n",
       "      <td>0.583768</td>\n",
       "      <td>0.605130</td>\n",
       "      <td>0.596162</td>\n",
       "      <td>0.613135</td>\n",
       "      <td>0.623367</td>\n",
       "      <td>0.558637</td>\n",
       "      <td>0.573906</td>\n",
       "      <td>0.602568</td>\n",
       "      <td>0.614566</td>\n",
       "      <td>0.600854</td>\n",
       "      <td>0.565631</td>\n",
       "      <td>0.599101</td>\n",
       "      <td>0.624915</td>\n",
       "      <td>0.607804</td>\n",
       "      <td>0.561531</td>\n",
       "      <td>0.613125</td>\n",
       "      <td>0.589686</td>\n",
       "      <td>0.628869</td>\n",
       "      <td>0.580209</td>\n",
       "      <td>0.651081</td>\n",
       "      <td>0.615733</td>\n",
       "      <td>0.635248</td>\n",
       "      <td>0.631385</td>\n",
       "      <td>0.591140</td>\n",
       "      <td>0.601464</td>\n",
       "      <td>0.616106</td>\n",
       "      <td>0.612062</td>\n",
       "      <td>0.579561</td>\n",
       "      <td>0.586088</td>\n",
       "      <td>0.608046</td>\n",
       "      <td>0.656887</td>\n",
       "      <td>0.584002</td>\n",
       "      <td>0.614045</td>\n",
       "      <td>0.566292</td>\n",
       "      <td>0.601129</td>\n",
       "      <td>0.582974</td>\n",
       "      <td>0.654019</td>\n",
       "      <td>0.597110</td>\n",
       "      <td>0.557391</td>\n",
       "      <td>0.603159</td>\n",
       "      <td>0.613683</td>\n",
       "      <td>0.605000</td>\n",
       "      <td>0.629298</td>\n",
       "      <td>0.576272</td>\n",
       "      <td>0.595940</td>\n",
       "      <td>0.608614</td>\n",
       "      <td>0.644617</td>\n",
       "      <td>0.604344</td>\n",
       "      <td>0.620463</td>\n",
       "      <td>0.663863</td>\n",
       "      <td>0.556484</td>\n",
       "      <td>0.586823</td>\n",
       "      <td>0.651367</td>\n",
       "      <td>0.609358</td>\n",
       "      <td>0.581796</td>\n",
       "      <td>0.563495</td>\n",
       "      <td>0.629659</td>\n",
       "      <td>0.574386</td>\n",
       "      <td>0.633798</td>\n",
       "      <td>0.615282</td>\n",
       "      <td>0.596368</td>\n",
       "      <td>0.644648</td>\n",
       "      <td>0.573956</td>\n",
       "      <td>0.585508</td>\n",
       "      <td>0.574858</td>\n",
       "      <td>0.662115</td>\n",
       "      <td>0.623209</td>\n",
       "      <td>0.606342</td>\n",
       "      <td>0.593959</td>\n",
       "      <td>0.603723</td>\n",
       "      <td>0.618104</td>\n",
       "      <td>0.574121</td>\n",
       "      <td>0.656192</td>\n",
       "      <td>0.559151</td>\n",
       "      <td>0.612774</td>\n",
       "      <td>0.630091</td>\n",
       "      <td>0.628818</td>\n",
       "      <td>0.553659</td>\n",
       "      <td>0.598340</td>\n",
       "      <td>0.608396</td>\n",
       "      <td>0.638836</td>\n",
       "      <td>0.638353</td>\n",
       "      <td>0.601460</td>\n",
       "      <td>0.594820</td>\n",
       "      <td>0.648661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0              1              2              3    \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.515985       0.527761       0.498848       0.516818   \n",
       "std         0.152716       0.159324       0.153221       0.154463   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.404160       0.405322       0.383234       0.400217   \n",
       "50%         0.508191       0.528530       0.491004       0.518970   \n",
       "75%         0.620387       0.645236       0.603369       0.632294   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 4              5              6              7    \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.517698       0.551997       0.501877       0.501123   \n",
       "std         0.139968       0.157852       0.142057       0.152988   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.414637       0.428839       0.396761       0.384659   \n",
       "50%         0.520277       0.556658       0.497967       0.497138   \n",
       "75%         0.619692       0.672246       0.599256       0.615573   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 8              9              10             11   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.522330       0.500931       0.536163       0.527899   \n",
       "std         0.161333       0.172013       0.139609       0.137933   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.396368       0.368839       0.434916       0.429363   \n",
       "50%         0.527633       0.509617       0.538523       0.526943   \n",
       "75%         0.650798       0.642599       0.637393       0.622586   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 12             13             14             15   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.483136       0.513056       0.468552       0.536975   \n",
       "std         0.155799       0.163695       0.161437       0.147757   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.376588       0.391069       0.342462       0.425603   \n",
       "50%         0.484384       0.515667       0.467309       0.537324   \n",
       "75%         0.598082       0.645173       0.592997       0.645134   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 16             17             18             19   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.502334       0.528249       0.494316       0.522030   \n",
       "std         0.147695       0.128088       0.149800       0.168049   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.393704       0.437048       0.378579       0.392535   \n",
       "50%         0.496489       0.528829       0.493405       0.522352   \n",
       "75%         0.601819       0.621482       0.604392       0.648008   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 20             21             22             23   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.509915       0.462462       0.483403       0.493736   \n",
       "std         0.160037       0.138612       0.142991       0.143748   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.382917       0.365953       0.376809       0.388580   \n",
       "50%         0.507352       0.462062       0.481882       0.490765   \n",
       "75%         0.634880       0.561072       0.583205       0.598570   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 24             25             26             27   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.431122       0.489763       0.505733       0.534426   \n",
       "std         0.144587       0.147594       0.148359       0.161927   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.317381       0.380647       0.398850       0.410089   \n",
       "50%         0.423311       0.487181       0.502182       0.535043   \n",
       "75%         0.541945       0.591182       0.605060       0.657013   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 28             29             30             31   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.514106       0.548852       0.537378       0.457673   \n",
       "std         0.116959       0.145895       0.157883       0.119786   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.433351       0.443880       0.416893       0.373207   \n",
       "50%         0.514385       0.543070       0.542848       0.456154   \n",
       "75%         0.597823       0.646176       0.661150       0.540416   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 32             33             34             35   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.537191       0.473129       0.465974       0.535088   \n",
       "std         0.164065       0.154159       0.156044       0.136152   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.411155       0.354796       0.350169       0.437158   \n",
       "50%         0.538156       0.466062       0.466248       0.537066   \n",
       "75%         0.662846       0.591646       0.584142       0.630908   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 36             37             38             39   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.460251       0.540522       0.469551       0.509196   \n",
       "std         0.139654       0.172430       0.118331       0.136228   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.362029       0.406898       0.385175       0.413359   \n",
       "50%         0.460733       0.542956       0.467354       0.509509   \n",
       "75%         0.562001       0.668787       0.551319       0.608796   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 40             41             42             43   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.534277       0.513974       0.512592       0.476419   \n",
       "std         0.155283       0.171118       0.150955       0.160823   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.419749       0.376220       0.402160       0.358942   \n",
       "50%         0.536303       0.510020       0.517156       0.473846   \n",
       "75%         0.643897       0.642352       0.628530       0.598307   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 44             45             46             47   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.427950       0.509088       0.503805       0.515045   \n",
       "std         0.133748       0.140510       0.142401       0.154741   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.345599       0.403854       0.400372       0.395364   \n",
       "50%         0.439028       0.510088       0.503022       0.514889   \n",
       "75%         0.521730       0.613135       0.602961       0.635376   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 48             49             50             51   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.493846       0.502872       0.477621       0.510900   \n",
       "std         0.145624       0.159593       0.140258       0.146897   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.387001       0.380389       0.381917       0.400354   \n",
       "50%         0.491985       0.502666       0.478557       0.511178   \n",
       "75%         0.600323       0.622053       0.577691       0.617776   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 52             53             54             55   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.530692       0.545097       0.504340       0.502771   \n",
       "std         0.119219       0.156500       0.152016       0.173084   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.446131       0.427843       0.390796       0.363395   \n",
       "50%         0.531322       0.549032       0.506814       0.498155   \n",
       "75%         0.613746       0.654443       0.617521       0.634811   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 56             57             58             59   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.527030       0.494826       0.476920       0.473422   \n",
       "std         0.134544       0.139025       0.129871       0.147310   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.430828       0.392614       0.386420       0.366795   \n",
       "50%         0.527751       0.493163       0.476693       0.468230   \n",
       "75%         0.628143       0.595023       0.568567       0.582923   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 60             61             62             63   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.522280       0.485550       0.511491       0.514804   \n",
       "std         0.154688       0.153124       0.143883       0.142893   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.403477       0.367575       0.408770       0.411533   \n",
       "50%         0.518937       0.486271       0.505848       0.515746   \n",
       "75%         0.630753       0.600446       0.607641       0.623090   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 64             65             66             67   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.498581       0.505421       0.532119       0.534220   \n",
       "std         0.155717       0.126978       0.133872       0.124957   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.385852       0.415357       0.434196       0.446166   \n",
       "50%         0.499476       0.506710       0.530681       0.533860   \n",
       "75%         0.616108       0.597852       0.626689       0.620653   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 68             69             70             71   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.472570       0.522108       0.508938       0.497665   \n",
       "std         0.135335       0.159141       0.146662       0.148503   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.380414       0.401211       0.396164       0.387716   \n",
       "50%         0.476460       0.522350       0.508916       0.496910   \n",
       "75%         0.570621       0.636067       0.621665       0.606047   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 72             73             74             75   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.490851       0.460515       0.513735       0.484571   \n",
       "std         0.148669       0.161602       0.135212       0.161690   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.383905       0.341178       0.418664       0.367809   \n",
       "50%         0.491074       0.453998       0.513906       0.484317   \n",
       "75%         0.599022       0.581641       0.610521       0.606826   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 76             77             78             79   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.493756       0.494717       0.544292       0.508565   \n",
       "std         0.141583       0.162223       0.151400       0.168255   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.396472       0.365005       0.427943       0.376597   \n",
       "50%         0.498160       0.490504       0.551169       0.506857   \n",
       "75%         0.596629       0.621107       0.659541       0.634649   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 80             81             82             83   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.491296       0.458804       0.511466       0.486112   \n",
       "std         0.153274       0.147921       0.163314       0.159764   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.386171       0.362042       0.385625       0.368035   \n",
       "50%         0.496793       0.466876       0.515065       0.486957   \n",
       "75%         0.607829       0.563126       0.638065       0.609808   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 84             85             86             87   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.517981       0.438198       0.462391       0.474071   \n",
       "std         0.147581       0.151036       0.143977       0.135336   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.408316       0.321336       0.365551       0.375965   \n",
       "50%         0.519694       0.435637       0.469555       0.472553   \n",
       "75%         0.626485       0.553976       0.565247       0.571637   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 88             89             90             91   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.456430       0.531300       0.507587       0.481287   \n",
       "std         0.154661       0.141951       0.147802       0.157736   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.345677       0.428254       0.393895       0.359719   \n",
       "50%         0.451047       0.528005       0.508324       0.480211   \n",
       "75%         0.572147       0.627820       0.621520       0.598946   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 92             93             94             95   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.491078       0.510832       0.506323       0.554257   \n",
       "std         0.149423       0.134401       0.171336       0.146616   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.381523       0.421500       0.371936       0.449674   \n",
       "50%         0.488719       0.517130       0.505376       0.550892   \n",
       "75%         0.601751       0.606847       0.635683       0.651851   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 96             97             98             99   \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.500742       0.503320       0.461295       0.528794   \n",
       "std         0.160738       0.157975       0.140187       0.150728   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.376239       0.383110       0.366444       0.418742   \n",
       "50%         0.500166       0.502852       0.464485       0.526770   \n",
       "75%         0.621765       0.623520       0.560954       0.631958   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 100            101            102            103  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.506508       0.469911       0.505504       0.487095   \n",
       "std         0.142750       0.174415       0.146964       0.152985   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.403924       0.336957       0.399099       0.371755   \n",
       "50%         0.509602       0.468760       0.505707       0.485117   \n",
       "75%         0.611182       0.605084       0.614299       0.600380   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 104            105            106            107  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.497137       0.546870       0.534303       0.453240   \n",
       "std         0.131931       0.132586       0.131911       0.145468   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.402604       0.453106       0.441344       0.345580   \n",
       "50%         0.496341       0.543733       0.533432       0.448973   \n",
       "75%         0.590352       0.636344       0.625583       0.559449   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 108            109            110            111  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.488595       0.482639       0.512987       0.541468   \n",
       "std         0.168729       0.165657       0.132941       0.142309   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.364793       0.355180       0.419251       0.435958   \n",
       "50%         0.490730       0.475920       0.508851       0.545083   \n",
       "75%         0.624063       0.611775       0.603083       0.642401   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 112            113            114            115  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.511192       0.504525       0.471960       0.482339   \n",
       "std         0.152092       0.164809       0.148504       0.139393   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.394085       0.381326       0.367610       0.385895   \n",
       "50%         0.519848       0.504969       0.472353       0.484260   \n",
       "75%         0.631587       0.632396       0.582081       0.583768   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 116            117            118            119  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.493240       0.496019       0.513170       0.502973   \n",
       "std         0.146309       0.137589       0.140135       0.164271   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.384171       0.395357       0.413113       0.377502   \n",
       "50%         0.498002       0.494390       0.515012       0.502623   \n",
       "75%         0.605130       0.596162       0.613135       0.623367   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 120            121            122            123  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.466064       0.454945       0.486760       0.495971   \n",
       "std         0.130715       0.155825       0.153525       0.156497   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.370895       0.335497       0.375127       0.387542   \n",
       "50%         0.464269       0.448501       0.486990       0.499995   \n",
       "75%         0.558637       0.573906       0.602568       0.614566   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 124            125            126            127  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.513785       0.460523       0.455726       0.499546   \n",
       "std         0.124145       0.146045       0.182593       0.161118   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.426730       0.348607       0.315985       0.378679   \n",
       "50%         0.515117       0.459208       0.447367       0.503320   \n",
       "75%         0.600854       0.565631       0.599101       0.624915   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 128            129            130            131  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.492822       0.457452       0.499680       0.476462   \n",
       "std         0.150149       0.142551       0.152384       0.144984   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.379086       0.355932       0.382883       0.369419   \n",
       "50%         0.494037       0.456297       0.501053       0.480252   \n",
       "75%         0.607804       0.561531       0.613125       0.589686   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 132            133            134            135  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.512760       0.492905       0.525405       0.513724   \n",
       "std         0.152113       0.127332       0.166750       0.144293   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.400457       0.401451       0.395609       0.405801   \n",
       "50%         0.513744       0.490677       0.528699       0.516519   \n",
       "75%         0.628869       0.580209       0.651081       0.615733   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 136            137            138            139  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.510128       0.505901       0.501010       0.506080   \n",
       "std         0.160893       0.163764       0.128992       0.133977   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.385614       0.377751       0.409684       0.412433   \n",
       "50%         0.509813       0.499336       0.501037       0.511414   \n",
       "75%         0.635248       0.631385       0.591140       0.601464   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 140            141            142            143  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.516195       0.481838       0.477298       0.479105   \n",
       "std         0.133847       0.165168       0.142302       0.147585   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.419279       0.357603       0.375208       0.377394   \n",
       "50%         0.520051       0.483754       0.474108       0.480244   \n",
       "75%         0.616106       0.612062       0.579561       0.586088   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 144            145            146            147  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.503210       0.540107       0.495444       0.511826   \n",
       "std         0.144910       0.160585       0.125882       0.142199   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.396458       0.418392       0.410772       0.407273   \n",
       "50%         0.503872       0.542717       0.498163       0.514168   \n",
       "75%         0.608046       0.656887       0.584002       0.614045   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 148            149            150            151  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.467937       0.518622       0.467816       0.528952   \n",
       "std         0.139072       0.115915       0.152168       0.169944   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.372199       0.438264       0.352619       0.396985   \n",
       "50%         0.471619       0.518477       0.462831       0.528367   \n",
       "75%         0.566292       0.601129       0.582974       0.654019   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 152            153            154            155  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.494399       0.439025       0.482565       0.511133   \n",
       "std         0.141332       0.155217       0.157817       0.149234   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.397275       0.323827       0.366944       0.402738   \n",
       "50%         0.496304       0.433003       0.483172       0.511340   \n",
       "75%         0.597110       0.557391       0.603159       0.613683   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 156            157            158            159  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.494235       0.519896       0.455447       0.499465   \n",
       "std         0.146933       0.159321       0.159841       0.138531   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.381294       0.401872       0.337772       0.399344   \n",
       "50%         0.490393       0.518327       0.449688       0.499273   \n",
       "75%         0.605000       0.629298       0.576272       0.595940   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 160            161            162            163  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.481598       0.535712       0.498333       0.514243   \n",
       "std         0.165237       0.149549       0.149255       0.154328   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.351550       0.424003       0.389326       0.397733   \n",
       "50%         0.475602       0.535854       0.497997       0.507982   \n",
       "75%         0.608614       0.644617       0.604344       0.620463   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 164            165            166            167  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.533231       0.449046       0.469414       0.555817   \n",
       "std         0.169934       0.145563       0.156038       0.136219   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.401771       0.345580       0.357842       0.459881   \n",
       "50%         0.540521       0.447744       0.468090       0.558255   \n",
       "75%         0.663863       0.556484       0.586823       0.651367   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 168            169            170            171  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.505918       0.486612       0.464213       0.535875   \n",
       "std         0.154248       0.130534       0.141643       0.133130   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.394978       0.394248       0.360059       0.439615   \n",
       "50%         0.497202       0.486008       0.464461       0.536480   \n",
       "75%         0.609358       0.581796       0.563495       0.629659   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 172            173            174            175  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.480749       0.530494       0.498602       0.476862   \n",
       "std         0.133356       0.143416       0.153333       0.164204   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.387193       0.426380       0.388034       0.348038   \n",
       "50%         0.479686       0.533095       0.498504       0.479087   \n",
       "75%         0.574386       0.633798       0.615282       0.596368   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 176            177            178            179  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.533144       0.443023       0.478042       0.490384   \n",
       "std         0.152222       0.168041       0.145138       0.131377   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.419961       0.310435       0.370089       0.398111   \n",
       "50%         0.538435       0.433711       0.479261       0.486670   \n",
       "75%         0.644648       0.573956       0.585508       0.574858   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 180            181            182            183  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.554668       0.520086       0.483625       0.476177   \n",
       "std         0.151238       0.152813       0.162235       0.156943   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.441633       0.408604       0.360846       0.358828   \n",
       "50%         0.556188       0.514575       0.483645       0.470247   \n",
       "75%         0.662115       0.623209       0.606342       0.593959   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 184            185            186            187  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.497133       0.522755       0.484323       0.530461   \n",
       "std         0.148255       0.132801       0.128819       0.164088   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.384277       0.427852       0.393252       0.404715   \n",
       "50%         0.493384       0.523775       0.481572       0.532237   \n",
       "75%         0.603723       0.618104       0.574121       0.656192   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 188            189            190            191  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.440038       0.500937       0.532601       0.521950   \n",
       "std         0.158303       0.148192       0.140158       0.155773   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.324635       0.389958       0.431376       0.404422   \n",
       "50%         0.434511       0.500410       0.531653       0.517279   \n",
       "75%         0.559151       0.612774       0.630091       0.628818   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 192            193            194            195  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.470032       0.502746       0.483477       0.536917   \n",
       "std         0.121015       0.132779       0.162998       0.149925   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.385063       0.411373       0.360573       0.429027   \n",
       "50%         0.467860       0.504894       0.481614       0.533706   \n",
       "75%         0.553659       0.598340       0.608396       0.638836   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 196            197            198            199  \n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000  \n",
       "mean        0.507605       0.488022       0.483899       0.527460  \n",
       "std         0.167666       0.152592       0.152223       0.154974  \n",
       "min         0.000000       0.000000       0.000000       0.000000  \n",
       "25%         0.376952       0.379516       0.380712       0.410436  \n",
       "50%         0.510850       0.484718       0.487100       0.534987  \n",
       "75%         0.638353       0.601460       0.594820       0.648661  \n",
       "max         1.000000       1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看归一化后的数据\n",
    "xx_train = pd.DataFrame(xx_train)\n",
    "xx_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 179902, 1: 20098})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(train_labels.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00733805 0.0066784 ]\n"
     ]
    }
   ],
   "source": [
    "#降维\n",
    "pca = PCA(n_components=2)\n",
    "xx_train = pca.fit_transform(xx_train)\n",
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FZsydw/od2/hk6NAck+hv3rzJiRMnaGl69P9Bi4oWemdm/fSLjaNLW968eSlatKhM9LmI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\npCdHktYUohpFRERw1M9GLlcTfjb5fGXKrvK48v3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26a002abe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xx_train[:100, 0], xx_train[:100, 1], c=train_labels[:100], cmap=plt.cm.RdYlBu,\n",
    "               edgecolors='k',label=\"训练集\")\n",
    "plt.show()\n",
    "# plt.scatter(x_train[:, 0], x_train[:, 1], c=y_test, cmap=cm_bright, alpha=0.5,\n",
    "#                edgecolors='k',label=\"测试集\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160000, 200)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#数据分割\n",
    "x_train,x_test,y_train,y_test = train_test_split(xx_train, train_labels, test_size=0.2, random_state=0)\n",
    "\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.svm import LinearSVC,SVC\n",
    "from sklearn.naive_bayes import MultinomialNB, BernoulliNB #引入多项式和伯努利的贝叶斯\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from time import time\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###  基准模型方法\n",
    "def benchmark(clf,name):\n",
    "    print (u'分类器：', clf)\n",
    "    \n",
    "    ##  设置最优参数，并使用5折交叉验证获取最优参数值\n",
    "    alpha_can = np.logspace(-2, 1, 10)\n",
    "    model = GridSearchCV(clf, param_grid={'alpha': alpha_can}, cv=5)\n",
    "    m = alpha_can.size\n",
    "    \n",
    "    ## 如果模型有一个参数是alpha，进行设置\n",
    "    if hasattr(clf, 'alpha'):\n",
    "        model.set_params(param_grid={'alpha': alpha_can})\n",
    "        m = alpha_can.size\n",
    "    ## 如果模型有一个k近邻的参数，进行设置\n",
    "    if hasattr(clf, 'n_neighbors'):\n",
    "        neighbors_can = np.arange(1, 15)\n",
    "        model.set_params(param_grid={'n_neighbors': neighbors_can})\n",
    "        m = neighbors_can.size\n",
    "    ## LinearSVC最优参数配置\n",
    "    if hasattr(clf, 'C'):\n",
    "        C_can = np.logspace(1, 3, 3)\n",
    "        model.set_params(param_grid={'C':C_can})\n",
    "        m = C_can.size\n",
    "    ## SVM最优参数设置\n",
    "    if hasattr(clf, 'C') & hasattr(clf, 'gamma'):\n",
    "        C_can = np.logspace(1, 3, 3)\n",
    "        gamma_can = np.logspace(-3, 0, 3)\n",
    "        model.set_params(param_grid={'C':C_can, 'gamma':gamma_can})\n",
    "        m = C_can.size * gamma_can.size\n",
    "    ## 设置深度相关参数，决策树\n",
    "    if hasattr(clf, 'max_depth'):\n",
    "        max_depth_can = np.arange(4, 10)\n",
    "        model.set_params(param_grid={'max_depth': max_depth_can})\n",
    "        m = max_depth_can.size\n",
    "    \n",
    "    ## 模型训练\n",
    "    t_start = time()\n",
    "    model.fit(x_train, y_train)\n",
    "    t_end = time()\n",
    "    t_train = (t_end - t_start) / (5*m)\n",
    "    print (u'5折交叉验证的训练时间为：%.3f秒/(5*%d)=%.3f秒' % ((t_end - t_start), m, t_train))\n",
    "    print (u'最优超参数为：', model.best_params_)\n",
    "    \n",
    "    ## 模型预测\n",
    "    t_start = time()\n",
    "    y_hat = model.predict(x_test)\n",
    "    t_end = time()\n",
    "    t_test = t_end - t_start\n",
    "    print (u'测试时间：%.3f秒' % t_test)\n",
    "    \n",
    "    ## 模型效果评估\n",
    "    train_acc = metrics.accuracy_score(y_train, model.predict(x_train))\n",
    "    test_acc = metrics.accuracy_score(y_test, y_hat)\n",
    "    print (u'训练集准确率：%.2f%%' % (100 * train_acc))\n",
    "    print (u'测试集准确率：%.2f%%' % (100 * test_acc))\n",
    "    \n",
    "    ## 返回结果(训练时间耗时，预测数据耗时，训练数据错误率，测试数据错误率, 名称)\n",
    "    return t_train, t_test, 1-train_acc, 1-test_acc, name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类器的比较：\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### 使用不同的分类器对数据进行比较\n",
    "print (u'分类器的比较：\\n')\n",
    "clfs = [\n",
    "    [RidgeClassifier(), 'Ridge'],\n",
    "    [BernoulliNB(), 'BernoulliNB'],\n",
    "#     [SVC(), 'SVM'],\n",
    "    [LinearSVC(loss='squared_hinge', penalty='l1', dual=False, tol=1e-4), 'LinearSVC-l1'],\n",
    "    [LinearSVC(loss='squared_hinge', penalty='l2', dual=False, tol=1e-4), 'LinearSVC-l2'],\n",
    "    [KNeighborsClassifier(), 'KNN'],\n",
    "    [RandomForestClassifier(n_estimators=200), 'RandomForest']\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类器的比较：\n",
      "\n",
      "分类器： RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
      "        max_iter=None, normalize=False, random_state=None, solver='auto',\n",
      "        tol=0.001)\n",
      "5折交叉验证的训练时间为：2.192秒/(5*10)=0.044秒\n",
      "最优超参数为： {'alpha': 0.01}\n",
      "测试时间：0.000秒\n",
      "训练集准确率：89.95%\n",
      "测试集准确率：89.96%\n",
      "\n",
      "\n",
      "分类器： KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
      "           weights='uniform')\n",
      "5折交叉验证的训练时间为：43.929秒/(5*14)=0.628秒\n",
      "最优超参数为： {'n_neighbors': 14}\n",
      "测试时间：0.204秒\n",
      "训练集准确率：89.95%\n",
      "测试集准确率：89.95%\n",
      "\n",
      "\n",
      "分类器： BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)\n",
      "5折交叉验证的训练时间为：2.395秒/(5*10)=0.048秒\n",
      "最优超参数为： {'alpha': 0.01}\n",
      "测试时间：0.002秒\n",
      "训练集准确率：89.95%\n",
      "测试集准确率：89.96%\n",
      "\n",
      "\n",
      "分类器： LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l1', random_state=None, tol=0.0001,\n",
      "     verbose=0)\n",
      "5折交叉验证的训练时间为：1.300秒/(5*3)=0.087秒\n",
      "最优超参数为： {'C': 10.0}\n",
      "测试时间：0.001秒\n",
      "训练集准确率：89.95%\n",
      "测试集准确率：89.96%\n",
      "\n",
      "\n",
      "分类器： LinearSVC(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0)\n",
      "5折交叉验证的训练时间为：1.388秒/(5*3)=0.093秒\n",
      "最优超参数为： {'C': 10.0}\n",
      "测试时间：0.001秒\n",
      "训练集准确率：89.95%\n",
      "测试集准确率：89.96%\n",
      "\n",
      "\n",
      "分类器： RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "5折交叉验证的训练时间为：550.807秒/(5*6)=18.360秒\n",
      "最优超参数为： {'max_depth': 4}\n",
      "测试时间：0.370秒\n",
      "训练集准确率：89.95%\n",
      "测试集准确率：89.96%\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## 将训练数据保存到一个列表中\n",
    "result = []\n",
    "for clf,name in clfs:\n",
    "    # 计算算法结果\n",
    "    a = benchmark(clf,name)\n",
    "    # 追加到一个列表中，方便进行展示操作\n",
    "    result.append(a)\n",
    "    print ('\\n')\n",
    "## 将列表转换为数组\n",
    "result = np.array(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### 获取需要画图的数据\n",
    "result = [[x[i] for x in result] for i in range(5)]\n",
    "training_time, test_time, training_err, test_err, clf_names = result\n",
    "\n",
    "training_time = np.array(training_time).astype(np.float)\n",
    "test_time = np.array(test_time).astype(np.float)\n",
    "training_err = np.array(training_err).astype(np.float)\n",
    "test_err = np.array(test_err).astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QkBCjZs2axsGDBw3AyMrKMiwWi5GRkZGnruDgYAMwli5dasTExBiGYRjnzp0zjh07Zlit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\nXnSrJicnR3PmzNGtt956xbEdDodKS0tVWloqPz8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26a02289f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 画图\n",
    "x = np.arange(len(training_time))\n",
    "plt.figure(figsize=(10, 7), facecolor='w')\n",
    "ax = plt.axes()\n",
    "b0 = ax.bar(x+0.1, training_err, width=0.2, color='#77E0A0')\n",
    "b1 = ax.bar(x+0.3, test_err, width=0.2, color='#8800FF')\n",
    "ax2 = ax.twinx()\n",
    "b2 = ax2.bar(x+0.5, training_time, width=0.2, color='#FFA0A0')\n",
    "b3 = ax2.bar(x+0.7, test_time, width=0.2, color='#FF8080')\n",
    "plt.xticks(x+0.5, clf_names)\n",
    "plt.legend([b0[0], b1[0], b2[0], b3[0]], (u'训练集错误率', u'测试集错误率', u'训练时间', u'测试时间'), loc='upper left', shadow=True)\n",
    "plt.title(u'桑坦德机器学习解释能力', fontsize=18)\n",
    "plt.xlabel(u'分类器名称')\n",
    "plt.grid(True)\n",
    "plt.tight_layout(2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# train_x = train.drop(['ID_code'],axis=1)\n",
    "# train_x = train_x.drop(['target'],axis=1)\n",
    "# train_y = train['target'].reset_index(drop=True)\n",
    "train_x=xx_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_y=train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rideg_clf......\n",
      "nb_clf......\n",
      "l1_clf......\n",
      "l2_clf......\n",
      "knn_clf......\n"
     ]
    }
   ],
   "source": [
    "print(\"rideg_clf......\")\n",
    "rideg_clf = clfs[0][0].fit(train_x, train_y)\n",
    "print(\"nb_clf......\")\n",
    "nb_clf = clfs[1][0].fit(train_x, train_y)\n",
    "print(\"l1_clf......\")\n",
    "l1_clf = clfs[2][0].fit(train_x, train_y)\n",
    "print(\"l2_clf......\")\n",
    "l2_clf = clfs[3][0].fit(train_x, train_y)\n",
    "print(\"knn_clf......\")\n",
    "knn_clf = clfs[4][0].fit(train_x, train_y)\n",
    "# print(\"rf_clf......\")\n",
    "# rf_clf = clfs[5][0].fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def blended_predictions(X):\n",
    "    a = rideg_clf.predict(X)\n",
    "    print(\"ridge..ok\",a.shape)\n",
    "#     b = knn_clf.predict(X)\n",
    "#     print(\"knn..ok\",b.shape)\n",
    "    c = nb_clf.predict(X)\n",
    "    print(\"nb..ok\",c.shape)\n",
    "    d = l1_clf.predict(X)\n",
    "    print(\"l1..ok\",d.shape)\n",
    "    e = l2_clf.predict(X)\n",
    "    print(\"l2..ok\",e.shape)\n",
    "    return ((0.3*a)+(0.3*c)+(0.1*d)+(0.3*e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 200)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x = test.drop(['ID_code'],axis=1)\n",
    "test_x = ss.transform(test_x)\n",
    "test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "      <th>128</th>\n",
       "      <th>129</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "      <th>140</th>\n",
       "      <th>141</th>\n",
       "      <th>142</th>\n",
       "      <th>143</th>\n",
       "      <th>144</th>\n",
       "      <th>145</th>\n",
       "      <th>146</th>\n",
       "      <th>147</th>\n",
       "      <th>148</th>\n",
       "      <th>149</th>\n",
       "      <th>150</th>\n",
       "      <th>151</th>\n",
       "      <th>152</th>\n",
       "      <th>153</th>\n",
       "      <th>154</th>\n",
       "      <th>155</th>\n",
       "      <th>156</th>\n",
       "      <th>157</th>\n",
       "      <th>158</th>\n",
       "      <th>159</th>\n",
       "      <th>160</th>\n",
       "      <th>161</th>\n",
       "      <th>162</th>\n",
       "      <th>163</th>\n",
       "      <th>164</th>\n",
       "      <th>165</th>\n",
       "      <th>166</th>\n",
       "      <th>167</th>\n",
       "      <th>168</th>\n",
       "      <th>169</th>\n",
       "      <th>170</th>\n",
       "      <th>171</th>\n",
       "      <th>172</th>\n",
       "      <th>173</th>\n",
       "      <th>174</th>\n",
       "      <th>175</th>\n",
       "      <th>176</th>\n",
       "      <th>177</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.535360</td>\n",
       "      <td>0.897837</td>\n",
       "      <td>0.628717</td>\n",
       "      <td>0.715833</td>\n",
       "      <td>0.548256</td>\n",
       "      <td>0.605894</td>\n",
       "      <td>0.574061</td>\n",
       "      <td>0.578182</td>\n",
       "      <td>0.611866</td>\n",
       "      <td>0.674016</td>\n",
       "      <td>0.474766</td>\n",
       "      <td>0.502258</td>\n",
       "      <td>0.438561</td>\n",
       "      <td>0.224288</td>\n",
       "      <td>0.468781</td>\n",
       "      <td>0.510767</td>\n",
       "      <td>0.408172</td>\n",
       "      <td>0.272646</td>\n",
       "      <td>0.500738</td>\n",
       "      <td>0.600578</td>\n",
       "      <td>0.248951</td>\n",
       "      <td>0.488649</td>\n",
       "      <td>0.347060</td>\n",
       "      <td>0.506248</td>\n",
       "      <td>0.272898</td>\n",
       "      <td>0.723250</td>\n",
       "      <td>0.369578</td>\n",
       "      <td>0.422438</td>\n",
       "      <td>0.540640</td>\n",
       "      <td>0.478163</td>\n",
       "      <td>0.654120</td>\n",
       "      <td>0.395085</td>\n",
       "      <td>0.700669</td>\n",
       "      <td>0.401839</td>\n",
       "      <td>0.436918</td>\n",
       "      <td>0.648217</td>\n",
       "      <td>0.359617</td>\n",
       "      <td>0.361495</td>\n",
       "      <td>0.445720</td>\n",
       "      <td>0.780374</td>\n",
       "      <td>0.367258</td>\n",
       "      <td>0.789947</td>\n",
       "      <td>0.526482</td>\n",
       "      <td>0.614721</td>\n",
       "      <td>0.528090</td>\n",
       "      <td>0.382464</td>\n",
       "      <td>0.546175</td>\n",
       "      <td>0.825746</td>\n",
       "      <td>0.525779</td>\n",
       "      <td>0.506291</td>\n",
       "      <td>0.540970</td>\n",
       "      <td>0.528447</td>\n",
       "      <td>0.475126</td>\n",
       "      <td>0.813736</td>\n",
       "      <td>0.474295</td>\n",
       "      <td>0.621119</td>\n",
       "      <td>0.628672</td>\n",
       "      <td>0.519463</td>\n",
       "      <td>0.217811</td>\n",
       "      <td>0.541627</td>\n",
       "      <td>0.514066</td>\n",
       "      <td>0.595841</td>\n",
       "      <td>0.394696</td>\n",
       "      <td>0.524412</td>\n",
       "      <td>0.557168</td>\n",
       "      <td>0.569421</td>\n",
       "      <td>0.553651</td>\n",
       "      <td>0.628244</td>\n",
       "      <td>0.352166</td>\n",
       "      <td>0.598889</td>\n",
       "      <td>0.618069</td>\n",
       "      <td>0.571190</td>\n",
       "      <td>0.286160</td>\n",
       "      <td>0.691240</td>\n",
       "      <td>0.583821</td>\n",
       "      <td>0.614524</td>\n",
       "      <td>0.516169</td>\n",
       "      <td>0.282829</td>\n",
       "      <td>0.374934</td>\n",
       "      <td>0.897291</td>\n",
       "      <td>0.567416</td>\n",
       "      <td>0.599483</td>\n",
       "      <td>0.857826</td>\n",
       "      <td>0.659442</td>\n",
       "      <td>0.556377</td>\n",
       "      <td>0.519196</td>\n",
       "      <td>0.447038</td>\n",
       "      <td>0.690740</td>\n",
       "      <td>0.733314</td>\n",
       "      <td>0.698963</td>\n",
       "      <td>0.607569</td>\n",
       "      <td>0.580345</td>\n",
       "      <td>0.580056</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>0.191669</td>\n",
       "      <td>0.437848</td>\n",
       "      <td>0.337834</td>\n",
       "      <td>0.650571</td>\n",
       "      <td>0.534360</td>\n",
       "      <td>0.739589</td>\n",
       "      <td>0.465785</td>\n",
       "      <td>0.607151</td>\n",
       "      <td>0.653008</td>\n",
       "      <td>0.423764</td>\n",
       "      <td>0.588344</td>\n",
       "      <td>0.429957</td>\n",
       "      <td>0.511187</td>\n",
       "      <td>0.341971</td>\n",
       "      <td>0.284615</td>\n",
       "      <td>0.354006</td>\n",
       "      <td>0.452239</td>\n",
       "      <td>0.505969</td>\n",
       "      <td>0.616953</td>\n",
       "      <td>0.727890</td>\n",
       "      <td>0.322900</td>\n",
       "      <td>0.447015</td>\n",
       "      <td>0.565736</td>\n",
       "      <td>0.329589</td>\n",
       "      <td>0.491590</td>\n",
       "      <td>0.716841</td>\n",
       "      <td>0.654827</td>\n",
       "      <td>0.464562</td>\n",
       "      <td>0.464881</td>\n",
       "      <td>0.555144</td>\n",
       "      <td>0.363901</td>\n",
       "      <td>0.462238</td>\n",
       "      <td>0.672580</td>\n",
       "      <td>0.663211</td>\n",
       "      <td>0.339046</td>\n",
       "      <td>0.499085</td>\n",
       "      <td>0.551106</td>\n",
       "      <td>0.595882</td>\n",
       "      <td>0.805201</td>\n",
       "      <td>0.730694</td>\n",
       "      <td>0.543017</td>\n",
       "      <td>0.585268</td>\n",
       "      <td>0.317316</td>\n",
       "      <td>0.508499</td>\n",
       "      <td>0.281854</td>\n",
       "      <td>0.326366</td>\n",
       "      <td>0.281695</td>\n",
       "      <td>0.358320</td>\n",
       "      <td>0.673513</td>\n",
       "      <td>0.516394</td>\n",
       "      <td>0.594301</td>\n",
       "      <td>0.465153</td>\n",
       "      <td>0.562317</td>\n",
       "      <td>0.351048</td>\n",
       "      <td>0.545416</td>\n",
       "      <td>0.518613</td>\n",
       "      <td>0.588630</td>\n",
       "      <td>0.518129</td>\n",
       "      <td>0.489165</td>\n",
       "      <td>0.303906</td>\n",
       "      <td>0.864667</td>\n",
       "      <td>0.592801</td>\n",
       "      <td>0.294747</td>\n",
       "      <td>0.528569</td>\n",
       "      <td>0.419397</td>\n",
       "      <td>0.525788</td>\n",
       "      <td>0.203130</td>\n",
       "      <td>0.383380</td>\n",
       "      <td>0.337188</td>\n",
       "      <td>0.359852</td>\n",
       "      <td>0.354554</td>\n",
       "      <td>0.554426</td>\n",
       "      <td>0.296260</td>\n",
       "      <td>0.623377</td>\n",
       "      <td>0.476344</td>\n",
       "      <td>0.327433</td>\n",
       "      <td>0.600754</td>\n",
       "      <td>0.598001</td>\n",
       "      <td>0.623231</td>\n",
       "      <td>0.750340</td>\n",
       "      <td>0.453933</td>\n",
       "      <td>0.513266</td>\n",
       "      <td>0.575857</td>\n",
       "      <td>0.336894</td>\n",
       "      <td>0.401178</td>\n",
       "      <td>0.476357</td>\n",
       "      <td>0.503094</td>\n",
       "      <td>0.554036</td>\n",
       "      <td>0.190180</td>\n",
       "      <td>0.307855</td>\n",
       "      <td>0.792989</td>\n",
       "      <td>0.601959</td>\n",
       "      <td>0.497131</td>\n",
       "      <td>0.342830</td>\n",
       "      <td>0.355245</td>\n",
       "      <td>0.639518</td>\n",
       "      <td>0.366928</td>\n",
       "      <td>0.749230</td>\n",
       "      <td>0.195180</td>\n",
       "      <td>0.473444</td>\n",
       "      <td>0.260824</td>\n",
       "      <td>0.810571</td>\n",
       "      <td>0.570992</td>\n",
       "      <td>0.788006</td>\n",
       "      <td>0.463751</td>\n",
       "      <td>0.447387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.408005</td>\n",
       "      <td>0.641132</td>\n",
       "      <td>0.533050</td>\n",
       "      <td>0.395056</td>\n",
       "      <td>0.355501</td>\n",
       "      <td>0.573148</td>\n",
       "      <td>0.601977</td>\n",
       "      <td>0.594479</td>\n",
       "      <td>0.294934</td>\n",
       "      <td>0.279021</td>\n",
       "      <td>0.491108</td>\n",
       "      <td>0.595236</td>\n",
       "      <td>0.556029</td>\n",
       "      <td>0.302647</td>\n",
       "      <td>0.322132</td>\n",
       "      <td>0.397789</td>\n",
       "      <td>0.368716</td>\n",
       "      <td>0.725805</td>\n",
       "      <td>0.759844</td>\n",
       "      <td>0.262548</td>\n",
       "      <td>0.478758</td>\n",
       "      <td>0.223696</td>\n",
       "      <td>0.531220</td>\n",
       "      <td>0.330933</td>\n",
       "      <td>0.646323</td>\n",
       "      <td>0.404373</td>\n",
       "      <td>0.476199</td>\n",
       "      <td>0.265890</td>\n",
       "      <td>0.540237</td>\n",
       "      <td>0.434109</td>\n",
       "      <td>0.482189</td>\n",
       "      <td>0.518720</td>\n",
       "      <td>0.508464</td>\n",
       "      <td>0.493471</td>\n",
       "      <td>0.236019</td>\n",
       "      <td>0.457651</td>\n",
       "      <td>0.664165</td>\n",
       "      <td>0.270211</td>\n",
       "      <td>0.531100</td>\n",
       "      <td>0.434984</td>\n",
       "      <td>0.848554</td>\n",
       "      <td>0.336283</td>\n",
       "      <td>0.372012</td>\n",
       "      <td>0.578463</td>\n",
       "      <td>0.274558</td>\n",
       "      <td>0.322050</td>\n",
       "      <td>0.613609</td>\n",
       "      <td>0.443207</td>\n",
       "      <td>0.387367</td>\n",
       "      <td>0.419099</td>\n",
       "      <td>0.264250</td>\n",
       "      <td>0.725559</td>\n",
       "      <td>0.628889</td>\n",
       "      <td>0.716899</td>\n",
       "      <td>0.641604</td>\n",
       "      <td>0.614652</td>\n",
       "      <td>0.719960</td>\n",
       "      <td>0.610674</td>\n",
       "      <td>0.431284</td>\n",
       "      <td>0.575749</td>\n",
       "      <td>0.272526</td>\n",
       "      <td>0.396471</td>\n",
       "      <td>0.544442</td>\n",
       "      <td>0.404584</td>\n",
       "      <td>0.443104</td>\n",
       "      <td>0.576588</td>\n",
       "      <td>0.369359</td>\n",
       "      <td>0.603056</td>\n",
       "      <td>0.693032</td>\n",
       "      <td>0.360985</td>\n",
       "      <td>0.359644</td>\n",
       "      <td>0.514060</td>\n",
       "      <td>0.437418</td>\n",
       "      <td>0.501670</td>\n",
       "      <td>0.391688</td>\n",
       "      <td>0.520241</td>\n",
       "      <td>0.509593</td>\n",
       "      <td>0.625038</td>\n",
       "      <td>0.622204</td>\n",
       "      <td>0.347947</td>\n",
       "      <td>0.412609</td>\n",
       "      <td>0.415164</td>\n",
       "      <td>0.707184</td>\n",
       "      <td>0.658394</td>\n",
       "      <td>0.305150</td>\n",
       "      <td>0.518952</td>\n",
       "      <td>0.534352</td>\n",
       "      <td>0.433025</td>\n",
       "      <td>0.420567</td>\n",
       "      <td>0.486676</td>\n",
       "      <td>0.497789</td>\n",
       "      <td>0.685130</td>\n",
       "      <td>0.584196</td>\n",
       "      <td>0.534445</td>\n",
       "      <td>0.196255</td>\n",
       "      <td>0.654235</td>\n",
       "      <td>0.424405</td>\n",
       "      <td>0.644280</td>\n",
       "      <td>0.509821</td>\n",
       "      <td>0.474549</td>\n",
       "      <td>0.582298</td>\n",
       "      <td>0.542406</td>\n",
       "      <td>0.732164</td>\n",
       "      <td>0.560361</td>\n",
       "      <td>0.602149</td>\n",
       "      <td>0.319171</td>\n",
       "      <td>0.386787</td>\n",
       "      <td>0.202410</td>\n",
       "      <td>0.066371</td>\n",
       "      <td>0.454799</td>\n",
       "      <td>0.381698</td>\n",
       "      <td>0.443689</td>\n",
       "      <td>0.543983</td>\n",
       "      <td>0.535423</td>\n",
       "      <td>0.627895</td>\n",
       "      <td>0.421495</td>\n",
       "      <td>0.459636</td>\n",
       "      <td>0.415042</td>\n",
       "      <td>0.488392</td>\n",
       "      <td>0.394781</td>\n",
       "      <td>0.385481</td>\n",
       "      <td>0.487648</td>\n",
       "      <td>0.544045</td>\n",
       "      <td>0.532454</td>\n",
       "      <td>0.575656</td>\n",
       "      <td>0.240485</td>\n",
       "      <td>0.699214</td>\n",
       "      <td>0.139511</td>\n",
       "      <td>0.282252</td>\n",
       "      <td>0.231693</td>\n",
       "      <td>0.280561</td>\n",
       "      <td>0.449017</td>\n",
       "      <td>0.721399</td>\n",
       "      <td>0.586413</td>\n",
       "      <td>0.728524</td>\n",
       "      <td>0.755353</td>\n",
       "      <td>0.398686</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.569083</td>\n",
       "      <td>0.488524</td>\n",
       "      <td>0.726047</td>\n",
       "      <td>0.788327</td>\n",
       "      <td>0.232968</td>\n",
       "      <td>0.272630</td>\n",
       "      <td>0.569826</td>\n",
       "      <td>0.130909</td>\n",
       "      <td>0.416644</td>\n",
       "      <td>0.531266</td>\n",
       "      <td>0.560986</td>\n",
       "      <td>0.311050</td>\n",
       "      <td>0.586858</td>\n",
       "      <td>0.496217</td>\n",
       "      <td>0.207312</td>\n",
       "      <td>0.386069</td>\n",
       "      <td>0.507955</td>\n",
       "      <td>0.601784</td>\n",
       "      <td>0.749389</td>\n",
       "      <td>0.753730</td>\n",
       "      <td>0.327043</td>\n",
       "      <td>0.633279</td>\n",
       "      <td>0.583423</td>\n",
       "      <td>0.562479</td>\n",
       "      <td>0.322164</td>\n",
       "      <td>0.574094</td>\n",
       "      <td>0.290827</td>\n",
       "      <td>0.338883</td>\n",
       "      <td>0.577115</td>\n",
       "      <td>0.786482</td>\n",
       "      <td>0.611021</td>\n",
       "      <td>0.400537</td>\n",
       "      <td>0.439658</td>\n",
       "      <td>0.354015</td>\n",
       "      <td>0.636112</td>\n",
       "      <td>0.411529</td>\n",
       "      <td>0.289998</td>\n",
       "      <td>0.361477</td>\n",
       "      <td>0.579863</td>\n",
       "      <td>0.205665</td>\n",
       "      <td>0.421433</td>\n",
       "      <td>0.613341</td>\n",
       "      <td>0.645227</td>\n",
       "      <td>0.733676</td>\n",
       "      <td>0.282714</td>\n",
       "      <td>0.373786</td>\n",
       "      <td>0.695041</td>\n",
       "      <td>0.386065</td>\n",
       "      <td>0.599665</td>\n",
       "      <td>0.553481</td>\n",
       "      <td>0.461309</td>\n",
       "      <td>0.536625</td>\n",
       "      <td>0.759502</td>\n",
       "      <td>0.593904</td>\n",
       "      <td>0.389199</td>\n",
       "      <td>0.728803</td>\n",
       "      <td>0.357802</td>\n",
       "      <td>0.601884</td>\n",
       "      <td>0.391146</td>\n",
       "      <td>0.647505</td>\n",
       "      <td>0.648642</td>\n",
       "      <td>0.265418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.254905</td>\n",
       "      <td>0.184314</td>\n",
       "      <td>0.465517</td>\n",
       "      <td>0.535820</td>\n",
       "      <td>0.447373</td>\n",
       "      <td>0.850517</td>\n",
       "      <td>0.417628</td>\n",
       "      <td>0.667081</td>\n",
       "      <td>0.582317</td>\n",
       "      <td>0.609142</td>\n",
       "      <td>0.406726</td>\n",
       "      <td>0.532598</td>\n",
       "      <td>0.197147</td>\n",
       "      <td>0.346807</td>\n",
       "      <td>0.706688</td>\n",
       "      <td>0.407587</td>\n",
       "      <td>0.534123</td>\n",
       "      <td>0.687597</td>\n",
       "      <td>0.608299</td>\n",
       "      <td>0.698087</td>\n",
       "      <td>0.487465</td>\n",
       "      <td>0.379796</td>\n",
       "      <td>0.505588</td>\n",
       "      <td>0.432095</td>\n",
       "      <td>0.225739</td>\n",
       "      <td>0.437817</td>\n",
       "      <td>0.220914</td>\n",
       "      <td>0.637764</td>\n",
       "      <td>0.772448</td>\n",
       "      <td>0.458341</td>\n",
       "      <td>0.508378</td>\n",
       "      <td>0.421012</td>\n",
       "      <td>0.766636</td>\n",
       "      <td>0.741816</td>\n",
       "      <td>0.617246</td>\n",
       "      <td>0.694988</td>\n",
       "      <td>0.544535</td>\n",
       "      <td>0.473420</td>\n",
       "      <td>0.398329</td>\n",
       "      <td>0.516476</td>\n",
       "      <td>0.302483</td>\n",
       "      <td>0.396612</td>\n",
       "      <td>0.160218</td>\n",
       "      <td>0.793361</td>\n",
       "      <td>0.414542</td>\n",
       "      <td>0.691687</td>\n",
       "      <td>0.442355</td>\n",
       "      <td>0.392499</td>\n",
       "      <td>0.416726</td>\n",
       "      <td>0.329108</td>\n",
       "      <td>0.305716</td>\n",
       "      <td>0.461703</td>\n",
       "      <td>0.575778</td>\n",
       "      <td>0.471923</td>\n",
       "      <td>0.653155</td>\n",
       "      <td>0.902387</td>\n",
       "      <td>0.650202</td>\n",
       "      <td>0.356923</td>\n",
       "      <td>0.259051</td>\n",
       "      <td>0.401882</td>\n",
       "      <td>0.405853</td>\n",
       "      <td>0.568348</td>\n",
       "      <td>0.649075</td>\n",
       "      <td>0.603779</td>\n",
       "      <td>0.784626</td>\n",
       "      <td>0.711114</td>\n",
       "      <td>0.430949</td>\n",
       "      <td>0.566177</td>\n",
       "      <td>0.291902</td>\n",
       "      <td>0.848367</td>\n",
       "      <td>0.444653</td>\n",
       "      <td>0.366390</td>\n",
       "      <td>0.498590</td>\n",
       "      <td>0.790565</td>\n",
       "      <td>0.525098</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.212697</td>\n",
       "      <td>0.518886</td>\n",
       "      <td>0.719645</td>\n",
       "      <td>0.659238</td>\n",
       "      <td>0.705110</td>\n",
       "      <td>0.433774</td>\n",
       "      <td>0.604056</td>\n",
       "      <td>0.457864</td>\n",
       "      <td>0.572678</td>\n",
       "      <td>0.304794</td>\n",
       "      <td>0.285508</td>\n",
       "      <td>0.553826</td>\n",
       "      <td>0.370707</td>\n",
       "      <td>0.177694</td>\n",
       "      <td>0.307556</td>\n",
       "      <td>0.514209</td>\n",
       "      <td>0.481759</td>\n",
       "      <td>0.290623</td>\n",
       "      <td>0.646231</td>\n",
       "      <td>0.441413</td>\n",
       "      <td>0.335472</td>\n",
       "      <td>0.391430</td>\n",
       "      <td>0.402444</td>\n",
       "      <td>0.733382</td>\n",
       "      <td>0.554611</td>\n",
       "      <td>0.496433</td>\n",
       "      <td>0.603115</td>\n",
       "      <td>0.342484</td>\n",
       "      <td>0.451332</td>\n",
       "      <td>0.554719</td>\n",
       "      <td>0.506098</td>\n",
       "      <td>0.615131</td>\n",
       "      <td>0.290138</td>\n",
       "      <td>0.520769</td>\n",
       "      <td>0.623434</td>\n",
       "      <td>0.776939</td>\n",
       "      <td>0.309303</td>\n",
       "      <td>0.533320</td>\n",
       "      <td>0.154954</td>\n",
       "      <td>0.491164</td>\n",
       "      <td>0.560703</td>\n",
       "      <td>0.721209</td>\n",
       "      <td>0.543727</td>\n",
       "      <td>0.289254</td>\n",
       "      <td>0.522969</td>\n",
       "      <td>0.536293</td>\n",
       "      <td>0.592977</td>\n",
       "      <td>0.517818</td>\n",
       "      <td>0.420226</td>\n",
       "      <td>0.533538</td>\n",
       "      <td>0.722860</td>\n",
       "      <td>0.575301</td>\n",
       "      <td>0.656090</td>\n",
       "      <td>0.396805</td>\n",
       "      <td>0.581630</td>\n",
       "      <td>0.541927</td>\n",
       "      <td>0.509932</td>\n",
       "      <td>0.418910</td>\n",
       "      <td>0.664002</td>\n",
       "      <td>0.839045</td>\n",
       "      <td>0.617795</td>\n",
       "      <td>0.306770</td>\n",
       "      <td>0.764344</td>\n",
       "      <td>0.265448</td>\n",
       "      <td>0.298778</td>\n",
       "      <td>0.444767</td>\n",
       "      <td>0.303879</td>\n",
       "      <td>0.511305</td>\n",
       "      <td>0.619482</td>\n",
       "      <td>0.336142</td>\n",
       "      <td>0.562097</td>\n",
       "      <td>0.898518</td>\n",
       "      <td>0.655938</td>\n",
       "      <td>0.622999</td>\n",
       "      <td>0.321180</td>\n",
       "      <td>0.701510</td>\n",
       "      <td>0.581600</td>\n",
       "      <td>0.427389</td>\n",
       "      <td>0.491752</td>\n",
       "      <td>0.507857</td>\n",
       "      <td>0.399785</td>\n",
       "      <td>0.870621</td>\n",
       "      <td>0.561381</td>\n",
       "      <td>0.507409</td>\n",
       "      <td>0.711012</td>\n",
       "      <td>0.459892</td>\n",
       "      <td>0.283116</td>\n",
       "      <td>0.318628</td>\n",
       "      <td>0.605894</td>\n",
       "      <td>0.274781</td>\n",
       "      <td>0.537354</td>\n",
       "      <td>0.555433</td>\n",
       "      <td>0.321966</td>\n",
       "      <td>0.668681</td>\n",
       "      <td>0.463120</td>\n",
       "      <td>0.593473</td>\n",
       "      <td>0.435708</td>\n",
       "      <td>0.475194</td>\n",
       "      <td>0.653146</td>\n",
       "      <td>0.717662</td>\n",
       "      <td>0.367704</td>\n",
       "      <td>0.683273</td>\n",
       "      <td>0.336089</td>\n",
       "      <td>0.578142</td>\n",
       "      <td>0.544842</td>\n",
       "      <td>0.349589</td>\n",
       "      <td>0.418995</td>\n",
       "      <td>0.641291</td>\n",
       "      <td>0.683527</td>\n",
       "      <td>0.536832</td>\n",
       "      <td>0.320663</td>\n",
       "      <td>0.647601</td>\n",
       "      <td>0.286006</td>\n",
       "      <td>0.567118</td>\n",
       "      <td>0.410181</td>\n",
       "      <td>0.705125</td>\n",
       "      <td>0.474327</td>\n",
       "      <td>0.464437</td>\n",
       "      <td>0.222877</td>\n",
       "      <td>0.775034</td>\n",
       "      <td>0.218283</td>\n",
       "      <td>0.182324</td>\n",
       "      <td>0.687383</td>\n",
       "      <td>0.232704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.408357</td>\n",
       "      <td>0.539775</td>\n",
       "      <td>0.574667</td>\n",
       "      <td>0.500064</td>\n",
       "      <td>0.325182</td>\n",
       "      <td>0.717406</td>\n",
       "      <td>0.424956</td>\n",
       "      <td>0.681060</td>\n",
       "      <td>0.671982</td>\n",
       "      <td>0.485690</td>\n",
       "      <td>0.526396</td>\n",
       "      <td>0.485843</td>\n",
       "      <td>0.506599</td>\n",
       "      <td>0.688437</td>\n",
       "      <td>0.556771</td>\n",
       "      <td>0.595141</td>\n",
       "      <td>0.543051</td>\n",
       "      <td>0.344032</td>\n",
       "      <td>0.244056</td>\n",
       "      <td>0.712028</td>\n",
       "      <td>0.543857</td>\n",
       "      <td>0.382893</td>\n",
       "      <td>0.416388</td>\n",
       "      <td>0.826649</td>\n",
       "      <td>0.553429</td>\n",
       "      <td>0.734415</td>\n",
       "      <td>0.632524</td>\n",
       "      <td>0.512110</td>\n",
       "      <td>0.472902</td>\n",
       "      <td>0.634585</td>\n",
       "      <td>0.812975</td>\n",
       "      <td>0.411141</td>\n",
       "      <td>0.719505</td>\n",
       "      <td>0.310431</td>\n",
       "      <td>0.284595</td>\n",
       "      <td>0.366821</td>\n",
       "      <td>0.634139</td>\n",
       "      <td>0.340663</td>\n",
       "      <td>0.633294</td>\n",
       "      <td>0.782754</td>\n",
       "      <td>0.569778</td>\n",
       "      <td>0.273511</td>\n",
       "      <td>0.762677</td>\n",
       "      <td>0.400655</td>\n",
       "      <td>0.376822</td>\n",
       "      <td>0.507273</td>\n",
       "      <td>0.623670</td>\n",
       "      <td>0.770805</td>\n",
       "      <td>0.707308</td>\n",
       "      <td>0.282917</td>\n",
       "      <td>0.450697</td>\n",
       "      <td>0.295404</td>\n",
       "      <td>0.492678</td>\n",
       "      <td>0.595240</td>\n",
       "      <td>0.509554</td>\n",
       "      <td>0.527007</td>\n",
       "      <td>0.480657</td>\n",
       "      <td>0.600462</td>\n",
       "      <td>0.433696</td>\n",
       "      <td>0.664742</td>\n",
       "      <td>0.636952</td>\n",
       "      <td>0.588427</td>\n",
       "      <td>0.505713</td>\n",
       "      <td>0.600589</td>\n",
       "      <td>0.684773</td>\n",
       "      <td>0.558771</td>\n",
       "      <td>0.555227</td>\n",
       "      <td>0.553436</td>\n",
       "      <td>0.280603</td>\n",
       "      <td>0.262585</td>\n",
       "      <td>0.318515</td>\n",
       "      <td>0.358873</td>\n",
       "      <td>0.370744</td>\n",
       "      <td>0.099749</td>\n",
       "      <td>0.429325</td>\n",
       "      <td>0.360328</td>\n",
       "      <td>0.609608</td>\n",
       "      <td>0.509723</td>\n",
       "      <td>0.556177</td>\n",
       "      <td>0.477263</td>\n",
       "      <td>0.734480</td>\n",
       "      <td>0.505021</td>\n",
       "      <td>0.322800</td>\n",
       "      <td>0.737634</td>\n",
       "      <td>0.675060</td>\n",
       "      <td>0.590805</td>\n",
       "      <td>0.392476</td>\n",
       "      <td>0.597638</td>\n",
       "      <td>0.587534</td>\n",
       "      <td>0.395766</td>\n",
       "      <td>0.452170</td>\n",
       "      <td>0.408804</td>\n",
       "      <td>0.391446</td>\n",
       "      <td>0.464541</td>\n",
       "      <td>0.508616</td>\n",
       "      <td>0.618827</td>\n",
       "      <td>0.595609</td>\n",
       "      <td>0.775708</td>\n",
       "      <td>0.394415</td>\n",
       "      <td>0.800931</td>\n",
       "      <td>0.635591</td>\n",
       "      <td>0.086691</td>\n",
       "      <td>0.191778</td>\n",
       "      <td>0.444849</td>\n",
       "      <td>0.624243</td>\n",
       "      <td>0.337966</td>\n",
       "      <td>0.428089</td>\n",
       "      <td>0.526582</td>\n",
       "      <td>0.464497</td>\n",
       "      <td>0.656808</td>\n",
       "      <td>0.680977</td>\n",
       "      <td>0.505220</td>\n",
       "      <td>0.674151</td>\n",
       "      <td>0.671714</td>\n",
       "      <td>0.328280</td>\n",
       "      <td>0.580456</td>\n",
       "      <td>0.449445</td>\n",
       "      <td>0.449533</td>\n",
       "      <td>0.717734</td>\n",
       "      <td>0.705793</td>\n",
       "      <td>0.418218</td>\n",
       "      <td>0.376179</td>\n",
       "      <td>0.702846</td>\n",
       "      <td>0.444006</td>\n",
       "      <td>0.515489</td>\n",
       "      <td>0.239016</td>\n",
       "      <td>0.576326</td>\n",
       "      <td>0.538715</td>\n",
       "      <td>0.487960</td>\n",
       "      <td>0.707802</td>\n",
       "      <td>0.573610</td>\n",
       "      <td>0.558832</td>\n",
       "      <td>0.195990</td>\n",
       "      <td>0.484609</td>\n",
       "      <td>0.814218</td>\n",
       "      <td>0.508346</td>\n",
       "      <td>0.448799</td>\n",
       "      <td>0.415220</td>\n",
       "      <td>0.383602</td>\n",
       "      <td>0.593747</td>\n",
       "      <td>0.751915</td>\n",
       "      <td>0.734350</td>\n",
       "      <td>0.370366</td>\n",
       "      <td>0.444968</td>\n",
       "      <td>0.688539</td>\n",
       "      <td>0.415107</td>\n",
       "      <td>0.413240</td>\n",
       "      <td>0.609776</td>\n",
       "      <td>0.353278</td>\n",
       "      <td>0.277542</td>\n",
       "      <td>0.275947</td>\n",
       "      <td>0.513909</td>\n",
       "      <td>0.410527</td>\n",
       "      <td>0.339609</td>\n",
       "      <td>0.420930</td>\n",
       "      <td>0.338059</td>\n",
       "      <td>0.554880</td>\n",
       "      <td>0.557460</td>\n",
       "      <td>0.375663</td>\n",
       "      <td>0.646921</td>\n",
       "      <td>0.412738</td>\n",
       "      <td>0.367529</td>\n",
       "      <td>0.321134</td>\n",
       "      <td>0.410805</td>\n",
       "      <td>0.277725</td>\n",
       "      <td>0.610001</td>\n",
       "      <td>0.333573</td>\n",
       "      <td>0.667937</td>\n",
       "      <td>0.622210</td>\n",
       "      <td>0.488234</td>\n",
       "      <td>0.695015</td>\n",
       "      <td>0.641878</td>\n",
       "      <td>0.213179</td>\n",
       "      <td>0.815211</td>\n",
       "      <td>0.376975</td>\n",
       "      <td>0.418638</td>\n",
       "      <td>0.472518</td>\n",
       "      <td>0.574557</td>\n",
       "      <td>0.615434</td>\n",
       "      <td>0.420469</td>\n",
       "      <td>0.905384</td>\n",
       "      <td>0.490988</td>\n",
       "      <td>0.579057</td>\n",
       "      <td>0.349720</td>\n",
       "      <td>0.423290</td>\n",
       "      <td>0.737676</td>\n",
       "      <td>0.255740</td>\n",
       "      <td>0.396538</td>\n",
       "      <td>0.362694</td>\n",
       "      <td>0.586592</td>\n",
       "      <td>0.727342</td>\n",
       "      <td>0.606357</td>\n",
       "      <td>0.447945</td>\n",
       "      <td>0.511046</td>\n",
       "      <td>0.337572</td>\n",
       "      <td>0.883857</td>\n",
       "      <td>0.558429</td>\n",
       "      <td>0.541227</td>\n",
       "      <td>0.339614</td>\n",
       "      <td>0.514331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.567520</td>\n",
       "      <td>0.586569</td>\n",
       "      <td>0.696941</td>\n",
       "      <td>0.588941</td>\n",
       "      <td>0.347404</td>\n",
       "      <td>0.481345</td>\n",
       "      <td>0.739656</td>\n",
       "      <td>0.235211</td>\n",
       "      <td>0.653272</td>\n",
       "      <td>0.441944</td>\n",
       "      <td>0.655655</td>\n",
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       "      <td>0.546520</td>\n",
       "      <td>0.528473</td>\n",
       "      <td>0.266748</td>\n",
       "      <td>0.539836</td>\n",
       "      <td>0.648344</td>\n",
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       "      <td>0.191589</td>\n",
       "      <td>0.446363</td>\n",
       "      <td>0.608420</td>\n",
       "      <td>0.652507</td>\n",
       "      <td>0.548732</td>\n",
       "      <td>0.599969</td>\n",
       "      <td>0.823331</td>\n",
       "      <td>0.414606</td>\n",
       "      <td>0.468199</td>\n",
       "      <td>0.658310</td>\n",
       "      <td>0.378639</td>\n",
       "      <td>0.470476</td>\n",
       "      <td>0.267460</td>\n",
       "      <td>0.571943</td>\n",
       "      <td>0.590596</td>\n",
       "      <td>0.513347</td>\n",
       "      <td>0.552789</td>\n",
       "      <td>0.608828</td>\n",
       "      <td>0.340105</td>\n",
       "      <td>0.499371</td>\n",
       "      <td>0.389654</td>\n",
       "      <td>0.394076</td>\n",
       "      <td>0.601787</td>\n",
       "      <td>0.717365</td>\n",
       "      <td>0.654948</td>\n",
       "      <td>0.671321</td>\n",
       "      <td>0.746895</td>\n",
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       "      <td>0.488067</td>\n",
       "      <td>0.348500</td>\n",
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       "      <td>0.498629</td>\n",
       "      <td>0.697144</td>\n",
       "      <td>0.376140</td>\n",
       "      <td>0.524097</td>\n",
       "      <td>0.512130</td>\n",
       "      <td>0.433643</td>\n",
       "      <td>0.518726</td>\n",
       "      <td>0.504935</td>\n",
       "      <td>0.447023</td>\n",
       "      <td>0.744268</td>\n",
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       "      <td>0.381882</td>\n",
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       "      <td>0.581195</td>\n",
       "      <td>0.697608</td>\n",
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       "      <td>0.432364</td>\n",
       "      <td>0.869518</td>\n",
       "      <td>0.347996</td>\n",
       "      <td>0.288839</td>\n",
       "      <td>0.531234</td>\n",
       "      <td>0.318586</td>\n",
       "      <td>0.605634</td>\n",
       "      <td>0.652515</td>\n",
       "      <td>0.615335</td>\n",
       "      <td>0.590484</td>\n",
       "      <td>0.296113</td>\n",
       "      <td>0.457754</td>\n",
       "      <td>0.580112</td>\n",
       "      <td>0.599140</td>\n",
       "      <td>0.487585</td>\n",
       "      <td>0.256668</td>\n",
       "      <td>0.138287</td>\n",
       "      <td>0.439668</td>\n",
       "      <td>0.795444</td>\n",
       "      <td>0.514519</td>\n",
       "      <td>0.742252</td>\n",
       "      <td>0.385460</td>\n",
       "      <td>0.536104</td>\n",
       "      <td>0.634312</td>\n",
       "      <td>0.544152</td>\n",
       "      <td>0.840844</td>\n",
       "      <td>0.427511</td>\n",
       "      <td>0.598479</td>\n",
       "      <td>0.385468</td>\n",
       "      <td>0.626673</td>\n",
       "      <td>0.521387</td>\n",
       "      <td>0.378866</td>\n",
       "      <td>0.696251</td>\n",
       "      <td>0.567920</td>\n",
       "      <td>0.679095</td>\n",
       "      <td>0.464833</td>\n",
       "      <td>0.485404</td>\n",
       "      <td>0.391568</td>\n",
       "      <td>0.543588</td>\n",
       "      <td>0.614738</td>\n",
       "      <td>0.454554</td>\n",
       "      <td>0.671189</td>\n",
       "      <td>0.495532</td>\n",
       "      <td>0.546568</td>\n",
       "      <td>0.230173</td>\n",
       "      <td>0.270600</td>\n",
       "      <td>0.673709</td>\n",
       "      <td>0.789633</td>\n",
       "      <td>0.629893</td>\n",
       "      <td>0.447792</td>\n",
       "      <td>0.412774</td>\n",
       "      <td>0.633607</td>\n",
       "      <td>0.614970</td>\n",
       "      <td>0.564529</td>\n",
       "      <td>0.479977</td>\n",
       "      <td>0.354777</td>\n",
       "      <td>0.487798</td>\n",
       "      <td>0.323335</td>\n",
       "      <td>0.570296</td>\n",
       "      <td>0.455658</td>\n",
       "      <td>0.449963</td>\n",
       "      <td>0.604821</td>\n",
       "      <td>0.650356</td>\n",
       "      <td>0.283936</td>\n",
       "      <td>0.575065</td>\n",
       "      <td>0.616164</td>\n",
       "      <td>0.335842</td>\n",
       "      <td>0.576475</td>\n",
       "      <td>0.766176</td>\n",
       "      <td>0.321676</td>\n",
       "      <td>0.730389</td>\n",
       "      <td>0.339309</td>\n",
       "      <td>0.601307</td>\n",
       "      <td>0.273951</td>\n",
       "      <td>0.624956</td>\n",
       "      <td>0.705728</td>\n",
       "      <td>0.623473</td>\n",
       "      <td>0.706229</td>\n",
       "      <td>0.667432</td>\n",
       "      <td>0.394013</td>\n",
       "      <td>0.576653</td>\n",
       "      <td>0.733823</td>\n",
       "      <td>0.370026</td>\n",
       "      <td>0.171184</td>\n",
       "      <td>0.477025</td>\n",
       "      <td>0.254604</td>\n",
       "      <td>0.330665</td>\n",
       "      <td>0.644035</td>\n",
       "      <td>0.460499</td>\n",
       "      <td>0.560077</td>\n",
       "      <td>0.577503</td>\n",
       "      <td>0.579972</td>\n",
       "      <td>0.249687</td>\n",
       "      <td>0.648616</td>\n",
       "      <td>0.583995</td>\n",
       "      <td>0.530901</td>\n",
       "      <td>0.543983</td>\n",
       "      <td>0.675889</td>\n",
       "      <td>0.413027</td>\n",
       "      <td>0.494910</td>\n",
       "      <td>0.441633</td>\n",
       "      <td>0.603611</td>\n",
       "      <td>0.365239</td>\n",
       "      <td>0.549301</td>\n",
       "      <td>0.346127</td>\n",
       "      <td>0.330086</td>\n",
       "      <td>0.347074</td>\n",
       "      <td>0.654547</td>\n",
       "      <td>0.594327</td>\n",
       "      <td>0.550204</td>\n",
       "      <td>0.335017</td>\n",
       "      <td>0.500366</td>\n",
       "      <td>0.357542</td>\n",
       "      <td>0.799344</td>\n",
       "      <td>0.459819</td>\n",
       "      <td>0.325040</td>\n",
       "      <td>0.284152</td>\n",
       "      <td>0.635420</td>\n",
       "      <td>0.563075</td>\n",
       "      <td>0.611288</td>\n",
       "      <td>0.417291</td>\n",
       "      <td>0.504277</td>\n",
       "      <td>0.564603</td>\n",
       "      <td>0.521822</td>\n",
       "      <td>0.278146</td>\n",
       "      <td>0.219809</td>\n",
       "      <td>0.385580</td>\n",
       "      <td>0.440485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  0.535360  0.897837  0.628717  0.715833  0.548256  0.605894  0.574061   \n",
       "1  0.408005  0.641132  0.533050  0.395056  0.355501  0.573148  0.601977   \n",
       "2  0.254905  0.184314  0.465517  0.535820  0.447373  0.850517  0.417628   \n",
       "3  0.408357  0.539775  0.574667  0.500064  0.325182  0.717406  0.424956   \n",
       "4  0.567520  0.586569  0.696941  0.588941  0.347404  0.481345  0.739656   \n",
       "\n",
       "        7         8         9         10        11        12        13   \\\n",
       "0  0.578182  0.611866  0.674016  0.474766  0.502258  0.438561  0.224288   \n",
       "1  0.594479  0.294934  0.279021  0.491108  0.595236  0.556029  0.302647   \n",
       "2  0.667081  0.582317  0.609142  0.406726  0.532598  0.197147  0.346807   \n",
       "3  0.681060  0.671982  0.485690  0.526396  0.485843  0.506599  0.688437   \n",
       "4  0.235211  0.653272  0.441944  0.655655  0.527041  0.546520  0.528473   \n",
       "\n",
       "        14        15        16        17        18        19        20   \\\n",
       "0  0.468781  0.510767  0.408172  0.272646  0.500738  0.600578  0.248951   \n",
       "1  0.322132  0.397789  0.368716  0.725805  0.759844  0.262548  0.478758   \n",
       "2  0.706688  0.407587  0.534123  0.687597  0.608299  0.698087  0.487465   \n",
       "3  0.556771  0.595141  0.543051  0.344032  0.244056  0.712028  0.543857   \n",
       "4  0.266748  0.539836  0.648344  0.697022  0.547140  0.550653  0.191589   \n",
       "\n",
       "        21        22        23        24        25        26        27   \\\n",
       "0  0.488649  0.347060  0.506248  0.272898  0.723250  0.369578  0.422438   \n",
       "1  0.223696  0.531220  0.330933  0.646323  0.404373  0.476199  0.265890   \n",
       "2  0.379796  0.505588  0.432095  0.225739  0.437817  0.220914  0.637764   \n",
       "3  0.382893  0.416388  0.826649  0.553429  0.734415  0.632524  0.512110   \n",
       "4  0.446363  0.608420  0.652507  0.548732  0.599969  0.823331  0.414606   \n",
       "\n",
       "        28        29        30        31        32        33        34   \\\n",
       "0  0.540640  0.478163  0.654120  0.395085  0.700669  0.401839  0.436918   \n",
       "1  0.540237  0.434109  0.482189  0.518720  0.508464  0.493471  0.236019   \n",
       "2  0.772448  0.458341  0.508378  0.421012  0.766636  0.741816  0.617246   \n",
       "3  0.472902  0.634585  0.812975  0.411141  0.719505  0.310431  0.284595   \n",
       "4  0.468199  0.658310  0.378639  0.470476  0.267460  0.571943  0.590596   \n",
       "\n",
       "        35        36        37        38        39        40        41   \\\n",
       "0  0.648217  0.359617  0.361495  0.445720  0.780374  0.367258  0.789947   \n",
       "1  0.457651  0.664165  0.270211  0.531100  0.434984  0.848554  0.336283   \n",
       "2  0.694988  0.544535  0.473420  0.398329  0.516476  0.302483  0.396612   \n",
       "3  0.366821  0.634139  0.340663  0.633294  0.782754  0.569778  0.273511   \n",
       "4  0.513347  0.552789  0.608828  0.340105  0.499371  0.389654  0.394076   \n",
       "\n",
       "        42        43        44        45        46        47        48   \\\n",
       "0  0.526482  0.614721  0.528090  0.382464  0.546175  0.825746  0.525779   \n",
       "1  0.372012  0.578463  0.274558  0.322050  0.613609  0.443207  0.387367   \n",
       "2  0.160218  0.793361  0.414542  0.691687  0.442355  0.392499  0.416726   \n",
       "3  0.762677  0.400655  0.376822  0.507273  0.623670  0.770805  0.707308   \n",
       "4  0.601787  0.717365  0.654948  0.671321  0.746895  0.665329  0.320042   \n",
       "\n",
       "        49        50        51        52        53        54        55   \\\n",
       "0  0.506291  0.540970  0.528447  0.475126  0.813736  0.474295  0.621119   \n",
       "1  0.419099  0.264250  0.725559  0.628889  0.716899  0.641604  0.614652   \n",
       "2  0.329108  0.305716  0.461703  0.575778  0.471923  0.653155  0.902387   \n",
       "3  0.282917  0.450697  0.295404  0.492678  0.595240  0.509554  0.527007   \n",
       "4  0.437267  0.488067  0.348500  0.587474  0.477735  0.498629  0.697144   \n",
       "\n",
       "        56        57        58        59        60        61        62   \\\n",
       "0  0.628672  0.519463  0.217811  0.541627  0.514066  0.595841  0.394696   \n",
       "1  0.719960  0.610674  0.431284  0.575749  0.272526  0.396471  0.544442   \n",
       "2  0.650202  0.356923  0.259051  0.401882  0.405853  0.568348  0.649075   \n",
       "3  0.480657  0.600462  0.433696  0.664742  0.636952  0.588427  0.505713   \n",
       "4  0.376140  0.524097  0.512130  0.433643  0.518726  0.504935  0.447023   \n",
       "\n",
       "        63        64        65        66        67        68        69   \\\n",
       "0  0.524412  0.557168  0.569421  0.553651  0.628244  0.352166  0.598889   \n",
       "1  0.404584  0.443104  0.576588  0.369359  0.603056  0.693032  0.360985   \n",
       "2  0.603779  0.784626  0.711114  0.430949  0.566177  0.291902  0.848367   \n",
       "3  0.600589  0.684773  0.558771  0.555227  0.553436  0.280603  0.262585   \n",
       "4  0.744268  0.486397  0.523560  0.381882  0.673192  0.619586  0.581195   \n",
       "\n",
       "        70        71        72        73        74        75        76   \\\n",
       "0  0.618069  0.571190  0.286160  0.691240  0.583821  0.614524  0.516169   \n",
       "1  0.359644  0.514060  0.437418  0.501670  0.391688  0.520241  0.509593   \n",
       "2  0.444653  0.366390  0.498590  0.790565  0.525098  0.475000  0.212697   \n",
       "3  0.318515  0.358873  0.370744  0.099749  0.429325  0.360328  0.609608   \n",
       "4  0.697608  0.709282  0.432364  0.869518  0.347996  0.288839  0.531234   \n",
       "\n",
       "        77        78        79        80        81        82        83   \\\n",
       "0  0.282829  0.374934  0.897291  0.567416  0.599483  0.857826  0.659442   \n",
       "1  0.625038  0.622204  0.347947  0.412609  0.415164  0.707184  0.658394   \n",
       "2  0.518886  0.719645  0.659238  0.705110  0.433774  0.604056  0.457864   \n",
       "3  0.509723  0.556177  0.477263  0.734480  0.505021  0.322800  0.737634   \n",
       "4  0.318586  0.605634  0.652515  0.615335  0.590484  0.296113  0.457754   \n",
       "\n",
       "        84        85        86        87        88        89        90   \\\n",
       "0  0.556377  0.519196  0.447038  0.690740  0.733314  0.698963  0.607569   \n",
       "1  0.305150  0.518952  0.534352  0.433025  0.420567  0.486676  0.497789   \n",
       "2  0.572678  0.304794  0.285508  0.553826  0.370707  0.177694  0.307556   \n",
       "3  0.675060  0.590805  0.392476  0.597638  0.587534  0.395766  0.452170   \n",
       "4  0.580112  0.599140  0.487585  0.256668  0.138287  0.439668  0.795444   \n",
       "\n",
       "        91        92        93        94        95        96        97   \\\n",
       "0  0.580345  0.580056  0.408143  0.191669  0.437848  0.337834  0.650571   \n",
       "1  0.685130  0.584196  0.534445  0.196255  0.654235  0.424405  0.644280   \n",
       "2  0.514209  0.481759  0.290623  0.646231  0.441413  0.335472  0.391430   \n",
       "3  0.408804  0.391446  0.464541  0.508616  0.618827  0.595609  0.775708   \n",
       "4  0.514519  0.742252  0.385460  0.536104  0.634312  0.544152  0.840844   \n",
       "\n",
       "        98        99        100       101       102       103       104  \\\n",
       "0  0.534360  0.739589  0.465785  0.607151  0.653008  0.423764  0.588344   \n",
       "1  0.509821  0.474549  0.582298  0.542406  0.732164  0.560361  0.602149   \n",
       "2  0.402444  0.733382  0.554611  0.496433  0.603115  0.342484  0.451332   \n",
       "3  0.394415  0.800931  0.635591  0.086691  0.191778  0.444849  0.624243   \n",
       "4  0.427511  0.598479  0.385468  0.626673  0.521387  0.378866  0.696251   \n",
       "\n",
       "        105       106       107       108       109       110       111  \\\n",
       "0  0.429957  0.511187  0.341971  0.284615  0.354006  0.452239  0.505969   \n",
       "1  0.319171  0.386787  0.202410  0.066371  0.454799  0.381698  0.443689   \n",
       "2  0.554719  0.506098  0.615131  0.290138  0.520769  0.623434  0.776939   \n",
       "3  0.337966  0.428089  0.526582  0.464497  0.656808  0.680977  0.505220   \n",
       "4  0.567920  0.679095  0.464833  0.485404  0.391568  0.543588  0.614738   \n",
       "\n",
       "        112       113       114       115       116       117       118  \\\n",
       "0  0.616953  0.727890  0.322900  0.447015  0.565736  0.329589  0.491590   \n",
       "1  0.543983  0.535423  0.627895  0.421495  0.459636  0.415042  0.488392   \n",
       "2  0.309303  0.533320  0.154954  0.491164  0.560703  0.721209  0.543727   \n",
       "3  0.674151  0.671714  0.328280  0.580456  0.449445  0.449533  0.717734   \n",
       "4  0.454554  0.671189  0.495532  0.546568  0.230173  0.270600  0.673709   \n",
       "\n",
       "        119       120       121       122       123       124       125  \\\n",
       "0  0.716841  0.654827  0.464562  0.464881  0.555144  0.363901  0.462238   \n",
       "1  0.394781  0.385481  0.487648  0.544045  0.532454  0.575656  0.240485   \n",
       "2  0.289254  0.522969  0.536293  0.592977  0.517818  0.420226  0.533538   \n",
       "3  0.705793  0.418218  0.376179  0.702846  0.444006  0.515489  0.239016   \n",
       "4  0.789633  0.629893  0.447792  0.412774  0.633607  0.614970  0.564529   \n",
       "\n",
       "        126       127       128       129       130       131       132  \\\n",
       "0  0.672580  0.663211  0.339046  0.499085  0.551106  0.595882  0.805201   \n",
       "1  0.699214  0.139511  0.282252  0.231693  0.280561  0.449017  0.721399   \n",
       "2  0.722860  0.575301  0.656090  0.396805  0.581630  0.541927  0.509932   \n",
       "3  0.576326  0.538715  0.487960  0.707802  0.573610  0.558832  0.195990   \n",
       "4  0.479977  0.354777  0.487798  0.323335  0.570296  0.455658  0.449963   \n",
       "\n",
       "        133       134       135       136       137       138       139  \\\n",
       "0  0.730694  0.543017  0.585268  0.317316  0.508499  0.281854  0.326366   \n",
       "1  0.586413  0.728524  0.755353  0.398686  0.271300  0.569083  0.488524   \n",
       "2  0.418910  0.664002  0.839045  0.617795  0.306770  0.764344  0.265448   \n",
       "3  0.484609  0.814218  0.508346  0.448799  0.415220  0.383602  0.593747   \n",
       "4  0.604821  0.650356  0.283936  0.575065  0.616164  0.335842  0.576475   \n",
       "\n",
       "        140       141       142       143       144       145       146  \\\n",
       "0  0.281695  0.358320  0.673513  0.516394  0.594301  0.465153  0.562317   \n",
       "1  0.726047  0.788327  0.232968  0.272630  0.569826  0.130909  0.416644   \n",
       "2  0.298778  0.444767  0.303879  0.511305  0.619482  0.336142  0.562097   \n",
       "3  0.751915  0.734350  0.370366  0.444968  0.688539  0.415107  0.413240   \n",
       "4  0.766176  0.321676  0.730389  0.339309  0.601307  0.273951  0.624956   \n",
       "\n",
       "        147       148       149       150       151       152       153  \\\n",
       "0  0.351048  0.545416  0.518613  0.588630  0.518129  0.489165  0.303906   \n",
       "1  0.531266  0.560986  0.311050  0.586858  0.496217  0.207312  0.386069   \n",
       "2  0.898518  0.655938  0.622999  0.321180  0.701510  0.581600  0.427389   \n",
       "3  0.609776  0.353278  0.277542  0.275947  0.513909  0.410527  0.339609   \n",
       "4  0.705728  0.623473  0.706229  0.667432  0.394013  0.576653  0.733823   \n",
       "\n",
       "        154       155       156       157       158       159       160  \\\n",
       "0  0.864667  0.592801  0.294747  0.528569  0.419397  0.525788  0.203130   \n",
       "1  0.507955  0.601784  0.749389  0.753730  0.327043  0.633279  0.583423   \n",
       "2  0.491752  0.507857  0.399785  0.870621  0.561381  0.507409  0.711012   \n",
       "3  0.420930  0.338059  0.554880  0.557460  0.375663  0.646921  0.412738   \n",
       "4  0.370026  0.171184  0.477025  0.254604  0.330665  0.644035  0.460499   \n",
       "\n",
       "        161       162       163       164       165       166       167  \\\n",
       "0  0.383380  0.337188  0.359852  0.354554  0.554426  0.296260  0.623377   \n",
       "1  0.562479  0.322164  0.574094  0.290827  0.338883  0.577115  0.786482   \n",
       "2  0.459892  0.283116  0.318628  0.605894  0.274781  0.537354  0.555433   \n",
       "3  0.367529  0.321134  0.410805  0.277725  0.610001  0.333573  0.667937   \n",
       "4  0.560077  0.577503  0.579972  0.249687  0.648616  0.583995  0.530901   \n",
       "\n",
       "        168       169       170       171       172       173       174  \\\n",
       "0  0.476344  0.327433  0.600754  0.598001  0.623231  0.750340  0.453933   \n",
       "1  0.611021  0.400537  0.439658  0.354015  0.636112  0.411529  0.289998   \n",
       "2  0.321966  0.668681  0.463120  0.593473  0.435708  0.475194  0.653146   \n",
       "3  0.622210  0.488234  0.695015  0.641878  0.213179  0.815211  0.376975   \n",
       "4  0.543983  0.675889  0.413027  0.494910  0.441633  0.603611  0.365239   \n",
       "\n",
       "        175       176       177       178       179       180       181  \\\n",
       "0  0.513266  0.575857  0.336894  0.401178  0.476357  0.503094  0.554036   \n",
       "1  0.361477  0.579863  0.205665  0.421433  0.613341  0.645227  0.733676   \n",
       "2  0.717662  0.367704  0.683273  0.336089  0.578142  0.544842  0.349589   \n",
       "3  0.418638  0.472518  0.574557  0.615434  0.420469  0.905384  0.490988   \n",
       "4  0.549301  0.346127  0.330086  0.347074  0.654547  0.594327  0.550204   \n",
       "\n",
       "        182       183       184       185       186       187       188  \\\n",
       "0  0.190180  0.307855  0.792989  0.601959  0.497131  0.342830  0.355245   \n",
       "1  0.282714  0.373786  0.695041  0.386065  0.599665  0.553481  0.461309   \n",
       "2  0.418995  0.641291  0.683527  0.536832  0.320663  0.647601  0.286006   \n",
       "3  0.579057  0.349720  0.423290  0.737676  0.255740  0.396538  0.362694   \n",
       "4  0.335017  0.500366  0.357542  0.799344  0.459819  0.325040  0.284152   \n",
       "\n",
       "        189       190       191       192       193       194       195  \\\n",
       "0  0.639518  0.366928  0.749230  0.195180  0.473444  0.260824  0.810571   \n",
       "1  0.536625  0.759502  0.593904  0.389199  0.728803  0.357802  0.601884   \n",
       "2  0.567118  0.410181  0.705125  0.474327  0.464437  0.222877  0.775034   \n",
       "3  0.586592  0.727342  0.606357  0.447945  0.511046  0.337572  0.883857   \n",
       "4  0.635420  0.563075  0.611288  0.417291  0.504277  0.564603  0.521822   \n",
       "\n",
       "        196       197       198       199  \n",
       "0  0.570992  0.788006  0.463751  0.447387  \n",
       "1  0.391146  0.647505  0.648642  0.265418  \n",
       "2  0.218283  0.182324  0.687383  0.232704  \n",
       "3  0.558429  0.541227  0.339614  0.514331  \n",
       "4  0.278146  0.219809  0.385580  0.440485  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x = pd.DataFrame(test_x)\n",
    "test_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ridge..ok (200000,)\n",
      "nb..ok (200000,)\n",
      "l1..ok (200000,)\n",
      "l2..ok (200000,)\n"
     ]
    }
   ],
   "source": [
    "y_predict= blended_predictions(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = pd.DataFrame(y_predict)\n",
    "from collections import Counter\n",
    "# Counter(y_predict)\n",
    "y_predict = np.where(y_predict>0,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.016565\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(y_predict>0)/len(y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.016565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.127635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "count  200000.000000\n",
       "mean        0.016565\n",
       "std         0.127635\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max         1.000000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = pd.DataFrame(y_predict)\n",
    "y_predict.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\"input/sample_submission.csv\")\n",
    "submission.iloc[:,1] = y_predict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200000, 2), (200000, 1))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.shape,y_predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission.iloc[:,1] = y_predict.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.iloc[:,1]=np.array(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_code</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test_0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test_1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test_2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test_3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test_4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>test_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>test_6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>test_7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>test_8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>test_9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>test_10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>test_11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>test_12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>test_13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>test_14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>test_15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>test_16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>test_17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>test_18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>test_19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>test_20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>test_21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>test_22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>test_23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>test_24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>test_25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>test_26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>test_27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>test_28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>test_29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>test_30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>test_31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>test_32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>test_33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>test_34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>test_35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>test_36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>test_37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>test_38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>test_39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>test_40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>test_41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>test_42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>test_43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>test_44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>test_45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>test_46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>test_47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>test_48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>test_49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>test_50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>test_51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>test_52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>test_53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>test_54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>test_55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>test_56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>test_57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>test_58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>test_59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>test_60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>test_61</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>test_62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>test_63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>test_64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>test_65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>test_66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>test_67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>test_68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>test_69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>test_70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>test_71</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>test_72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>test_73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>test_74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>test_75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>test_76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>test_77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>test_78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>test_79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>test_80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>test_81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>test_82</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>test_83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>test_84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>test_85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>test_86</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>test_87</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>test_88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>test_89</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>test_90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>test_91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>test_92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>test_93</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>test_94</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>test_95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>test_96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>test_97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>test_98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>test_99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID_code  target\n",
       "0    test_0       0\n",
       "1    test_1       0\n",
       "2    test_2       0\n",
       "3    test_3       0\n",
       "4    test_4       0\n",
       "5    test_5       0\n",
       "6    test_6       0\n",
       "7    test_7       0\n",
       "8    test_8       0\n",
       "9    test_9       0\n",
       "10  test_10       0\n",
       "11  test_11       0\n",
       "12  test_12       0\n",
       "13  test_13       0\n",
       "14  test_14       0\n",
       "15  test_15       0\n",
       "16  test_16       0\n",
       "17  test_17       0\n",
       "18  test_18       0\n",
       "19  test_19       0\n",
       "20  test_20       0\n",
       "21  test_21       0\n",
       "22  test_22       0\n",
       "23  test_23       0\n",
       "24  test_24       0\n",
       "25  test_25       0\n",
       "26  test_26       0\n",
       "27  test_27       0\n",
       "28  test_28       0\n",
       "29  test_29       0\n",
       "30  test_30       0\n",
       "31  test_31       0\n",
       "32  test_32       0\n",
       "33  test_33       0\n",
       "34  test_34       0\n",
       "35  test_35       0\n",
       "36  test_36       0\n",
       "37  test_37       0\n",
       "38  test_38       0\n",
       "39  test_39       0\n",
       "40  test_40       0\n",
       "41  test_41       0\n",
       "42  test_42       0\n",
       "43  test_43       0\n",
       "44  test_44       0\n",
       "45  test_45       0\n",
       "46  test_46       0\n",
       "47  test_47       0\n",
       "48  test_48       0\n",
       "49  test_49       0\n",
       "50  test_50       0\n",
       "51  test_51       0\n",
       "52  test_52       0\n",
       "53  test_53       0\n",
       "54  test_54       0\n",
       "55  test_55       0\n",
       "56  test_56       0\n",
       "57  test_57       0\n",
       "58  test_58       0\n",
       "59  test_59       0\n",
       "60  test_60       0\n",
       "61  test_61       0\n",
       "62  test_62       0\n",
       "63  test_63       0\n",
       "64  test_64       0\n",
       "65  test_65       0\n",
       "66  test_66       0\n",
       "67  test_67       0\n",
       "68  test_68       0\n",
       "69  test_69       0\n",
       "70  test_70       0\n",
       "71  test_71       0\n",
       "72  test_72       0\n",
       "73  test_73       0\n",
       "74  test_74       0\n",
       "75  test_75       0\n",
       "76  test_76       0\n",
       "77  test_77       0\n",
       "78  test_78       0\n",
       "79  test_79       0\n",
       "80  test_80       0\n",
       "81  test_81       0\n",
       "82  test_82       0\n",
       "83  test_83       0\n",
       "84  test_84       0\n",
       "85  test_85       0\n",
       "86  test_86       0\n",
       "87  test_87       0\n",
       "88  test_88       0\n",
       "89  test_89       0\n",
       "90  test_90       0\n",
       "91  test_91       0\n",
       "92  test_92       0\n",
       "93  test_93       0\n",
       "94  test_94       0\n",
       "95  test_95       0\n",
       "96  test_96       0\n",
       "97  test_97       0\n",
       "98  test_98       0\n",
       "99  test_99       0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission_regression2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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